Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis,
A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M.,
Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R.,
Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I.,
Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden,
P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015). TensorFlow: Large-scale
machine learning on heterogeneous systems. Software available from 25,
214, 446
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J. (1985). A learning algorithm for
Boltzmann machines. Cognitive Science, 9, 147–169. 570, 654
Alain, G. and Bengio, Y. (2013). What regularized auto-encoders learn from the data
generating distribution. In ICLR’2013, arXiv:1211.4246 . 507, 513, 514, 521
Alain, G., Bengio, Y., Yao, L., Éric Thibodeau-Laufer, Yosinski, J., and Vincent, P. (2015).
GSNs: Generative stochastic networks. arXiv:1503.05571. 510, 713
Anderson, E. (1935). The Irises of the Gaspé Peninsula. Bulletin of the American Iris
Society, 59, 2–5. 21
Ba, J., Mnih, V., and Kavukcuoglu, K. (2014). Multiple object recognition with visual
attention. arXiv:1412.7755 . 691
Bachman, P. and Precup, D. (2015). Variational generative stochastic networks with
collaborative shaping. In Proceedings of the 32nd International Conference on Machine
Learning, ICML 2015, Lille, France, 6-11 July 2015 , pages 1964–1972. 717
Bacon, P.-L., Bengio, E., Pineau, J., and Precup, D. (2015). Conditional computation in
neural networks using a decision-theoretic approach. In 2nd Multidisciplinary Conference
on Reinforcement Learning and Decision Making (RLDM 2015). 450
Bagnell, J. A. and Bradley, D. M. (2009). Differentiable sparse coding. In D. Koller,
D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information
Processing Systems 21 (NIPS’08), pages 113–120. 498
Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural machine translation by jointly
learning to align and translate. In ICLR’2015, arXiv:1409.0473 . 25, 101, 397, 418, 420,
465, 475, 476
Bahl, L. R., Brown, P., de Souza, P. V., and Mercer, R. L. (1987). Speech recognition
with continuous-parameter hidden Markov models. Computer, Speech and Language,
219–234. 458
Baldi, P. and Hornik, K. (1989). Neural networks and principal component analysis:
Learning from examples without local minima. Neural Networks, 2, 53–58. 286
Baldi, P., Brunak, S., Frasconi, P., Soda, G., and Pollastri, G. (1999). Exploiting the
past and the future in protein secondary structure prediction. Bioinformatics,
937–946. 395
Baldi, P., Sadowski, P., and Whiteson, D. (2014). Searching for exotic particles in
high-energy physics with deep learning. Nature communications, 5. 26
Ballard, D. H., Hinton, G. E., and Sejnowski, T. J. (1983). Parallel vision computation.
Nature. 452
Barlow, H. B. (1989). Unsupervised learning. Neural Computation, 1, 295–311. 147
Barron, A. E. (1993). Universal approximation bounds for superpositions of a sigmoidal
function. IEEE Trans. on Information Theory, 39, 930–945. 199
Bartholomew, D. J. (1987). Latent variable models and factor analysis. Oxford University
Press. 490
Basilevsky, A. (1994). Statistical Factor Analysis and Related Methods: Theory and
Applications. Wiley. 490
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A.,
Bouchard, N., and Bengio, Y. (2012). Theano: new features and speed improvements.
Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop. 25, 82, 214,
222, 446
Basu, S. and Christensen, J. (2013). Teaching classification boundaries to humans. In
AAAI’2013 . 329
Baxter, J. (1995). Learning internal representations. In Proceedings of the 8th International
Conference on Computational Learning Theory (COLT’95), pages 311–320, Santa Cruz,
California. ACM Press. 245
Bayer, J. and Osendorfer, C. (2014). Learning stochastic recurrent networks. ArXiv
e-prints. 265
Becker, S. and Hinton, G. (1992). A self-organizing neural network that discovers surfaces
in random-dot stereograms. Nature, 355, 161–163. 541
Behnke, S. (2001). Learning iterative image reconstruction in the neural abstraction
pyramid. Int. J. Computational Intelligence and Applications, 1(4), 427–438. 515
Beiu, V., Quintana, J. M., and Avedillo, M. J. (2003). VLSI implementations of threshold
logic-a comprehensive survey. Neural Networks, IEEE Transactions on,
(5), 1217–
1243. 451
Belkin, M. and Niyogi, P. (2002). Laplacian eigenmaps and spectral techniques for
embedding and clustering. In T. Dietterich, S. Becker, and Z. Ghahramani, editors,
Advances in Neural Information Processing Systems 14 (NIPS’01), Cambridge, MA.
MIT Press. 244
Belkin, M. and Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and
data representation. Neural Computation, 15(6), 1373–1396. 164, 518
Bengio, E., Bacon, P.-L., Pineau, J., and Precup, D. (2015a). Conditional computation in
neural networks for faster models. arXiv:1511.06297. 450
Bengio, S. and Bengio, Y. (2000a). Taking on the curse of dimensionality in joint
distributions using neural networks. IEEE Transactions on Neural Networks, special
issue on Data Mining and Knowledge Discovery, 11(3), 550–557. 707
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N. (2015b). Scheduled sampling for
sequence prediction with recurrent neural networks. Technical report, arXiv:1506.03099.
Bengio, Y. (1991). Artificial Neural Networks and their Application to Sequence Recognition.
Ph.D. thesis, McGill University, (Computer Science), Montreal, Canada. 407
Bengio, Y. (2000). Gradient-based optimization of hyperparameters. Neural Computation,
12(8), 1889–1900. 435
Bengio, Y. (2002). New distributed probabilistic language models. Technical Report 1215,
Dept. IRO, Université de Montréal. 467
Bengio, Y. (2009). Learning deep architectures for AI . Now Publishers. 201, 622
Bengio, Y. (2013). Deep learning of representations: looking forward. In Statistical
Language and Speech Processing, volume 7978 of Lecture Notes in Computer Science,
pages 1–37. Springer, also in arXiv at 448
Bengio, Y. (2015). Early inference in energy-based models approximates back-propagation.
Technical Report arXiv:1510.02777, Universite de Montreal. 656
Bengio, Y. and Bengio, S. (2000b). Modeling high-dimensional discrete data with multi-
layer neural networks. In NIPS 12 , pages 400–406. MIT Press. 705, 707, 708, 710
Bengio, Y. and Delalleau, O. (2009). Justifying and generalizing contrastive divergence.
Neural Computation, 21(6), 1601–1621. 513, 611
Bengio, Y. and Grandvalet, Y. (2004). No unbiased estimator of the variance of k-fold
cross-validation. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural
Information Processing Systems 16 (NIPS’03), Cambridge, MA. MIT Press, Cambridge.
Bengio, Y. and LeCun, Y. (2007). Scaling learning algorithms towards AI. In Large Scale
Kernel Machines. 19
Bengio, Y. and Monperrus, M. (2005). Non-local manifold tangent learning. In L. Saul,
Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems
17 (NIPS’04), pages 129–136. MIT Press. 160, 519
Bengio, Y. and Sénécal, J.-S. (2003). Quick training of probabilistic neural nets by
importance sampling. In Proceedings of AISTATS 2003 . 470
Bengio, Y. and Sénécal, J.-S. (2008). Adaptive importance sampling to accelerate training
of a neural probabilistic language model. IEEE Trans. Neural Networks,
(4), 713–722.
Bengio, Y., De Mori, R., Flammia, G., and Kompe, R. (1991). Phonetically motivated
acoustic parameters for continuous speech recognition using artificial neural networks.
In Proceedings of EuroSpeech’91 . 27, 459
Bengio, Y., De Mori, R., Flammia, G., and Kompe, R. (1992). Neural network-Gaussian
mixture hybrid for speech recognition or density estimation. In NIPS 4 , pages 175–182.
Morgan Kaufmann. 459
Bengio, Y., Frasconi, P., and Simard, P. (1993). The problem of learning long-term
dependencies in recurrent networks. In IEEE International Conference on Neural
Networks, pages 1183–1195, San Francisco. IEEE Press. (invited paper). 403
Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with
gradient descent is difficult. IEEE Tr. Neural Nets. 18, 401, 403, 411
Bengio, Y., Latendresse, S., and Dugas, C. (1999). Gradient-based learning of hyper-
parameters. Learning Conference, Snowbird. 435
Bengio, Y., Ducharme, R., and Vincent, P. (2001). A neural probabilistic language model.
In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS’2000 , pages 932–938. MIT
Press. 18, 447, 464, 466, 472, 477, 482
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C. (2003). A neural probabilistic
language model. JMLR, 3, 1137–1155. 466, 472
Bengio, Y., Le Roux, N., Vincent, P., Delalleau, O., and Marcotte, P. (2006a). Convex
neural networks. In NIPS’2005 , pages 123–130. 258
Bengio, Y., Delalleau, O., and Le Roux, N. (2006b). The curse of highly variable functions
for local kernel machines. In NIPS’2005 . 158
Bengio, Y., Larochelle, H., and Vincent, P. (2006c). Non-local manifold Parzen windows.
In NIPS’2005 . MIT Press. 160, 520
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2007). Greedy layer-wise
training of deep networks. In NIPS’2006 . 14, 19, 201, 323, 324, 528, 530
Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009). Curriculum learning. In
ICML’09 . 328
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. (2013a). Better mixing via deep
representations. In ICML’2013 . 604
Bengio, Y., Léonard, N., and Courville, A. (2013b). Estimating or propagating gradients
through stochastic neurons for conditional computation. arXiv:1308.3432. 448, 450,
689, 691
Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013c). Generalized denoising auto-
encoders as generative models. In NIPS’2013 . 507, 711, 714
Bengio, Y., Courville, A., and Vincent, P. (2013d). Representation learning: A review and
new perspectives. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI),
35(8), 1798–1828. 555
Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2014). Deep generative
stochastic networks trainable by backprop. In ICML’2014 . 711, 712, 713, 714, 715
Bennett, C. (1976). Efficient estimation of free energy differences from Monte Carlo data.
Journal of Computational Physics, 22(2), 245–268. 628
Bennett, J. and Lanning, S. (2007). The Netflix prize. 479
Berger, A. L., Della Pietra, V. J., and Della Pietra, S. A. (1996). A maximum entropy
approach to natural language processing. Computational Linguistics, 22, 39–71. 473
Berglund, M. and Raiko, T. (2013). Stochastic gradient estimate variance in contrastive
divergence and persistent contrastive divergence. CoRR, abs/1312.6002. 614
Bergstra, J. (2011). Incorporating Complex Cells into Neural Networks for Pattern
Classification. Ph.D. thesis, Université de Montréal. 255
Bergstra, J. and Bengio, Y. (2009). Slow, decorrelated features for pretraining complex
cell-like networks. In NIPS’2009 . 494
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. J.
Machine Learning Res., 13, 281–305. 433, 434, 435
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian,
J., Warde-Farley, D., and Bengio, Y. (2010). Theano: a CPU and GPU math expression
compiler. In Proc. SciPy. 25, 82, 214, 222, 446
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011). Algorithms for hyper-parameter
optimization. In NIPS’2011 . 436
Berkes, P. and Wiskott, L. (2005). Slow feature analysis yields a rich repertoire of complex
cell properties. Journal of Vision, 5(6), 579–602. 495
Bertsekas, D. P. and Tsitsiklis, J. (1996). Neuro-Dynamic Programming. Athena Scientific.
Besag, J. (1975). Statistical analysis of non-lattice data. The Statistician,
(3), 179–195.
Bishop, C. M. (1994). Mixture density networks. 189
Bishop, C. M. (1995a). Regularization and complexity control in feed-forward networks.
In Proceedings International Conference on Artificial Neural Networks ICANN’95 ,
volume 1, page 141–148. 242, 250
Bishop, C. M. (1995b). Training with noise is equivalent to Tikhonov regularization.
Neural Computation, 7(1), 108–116. 242
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. 98, 146
Blum, A. L. and Rivest, R. L. (1992). Training a 3-node neural network is NP-complete.
Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. K. (1989). Learnability and
the Vapnik–Chervonenkis dimension. Journal of the ACM , 36(4), 929––865. 114
Bonnet, G. (1964). Transformations des signaux aléatoires à travers les systèmes non
linéaires sans mémoire. Annales des Télécommunications, 19(9–10), 203–220. 689
Bordes, A., Weston, J., Collobert, R., and Bengio, Y. (2011). Learning structured
embeddings of knowledge bases. In AAAI 2011 . 484
Bordes, A., Glorot, X., Weston, J., and Bengio, Y. (2012). Joint learning of words and
meaning representations for open-text semantic parsing. AISTATS’2012 . 401, 484, 485
Bordes, A., Glorot, X., Weston, J., and Bengio, Y. (2013a). A semantic matching energy
function for learning with multi-relational data. Machine Learning: Special Issue on
Learning Semantics. 483
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013b).
Translating embeddings for modeling multi-relational data. In C. Burges, L. Bottou,
M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information
Processing Systems 26 , pages 2787–2795. Curran Associates, Inc. 484
Bornschein, J. and Bengio, Y. (2015). Reweighted wake-sleep. In ICLR’2015,
arXiv:1406.2751 . 693
Bornschein, J., Shabanian, S., Fischer, A., and Bengio, Y. (2015). Training bidirectional
Helmholtz machines. Technical report, arXiv:1506.03877. 693
Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for opti-
mal margin classifiers. In COLT ’92: Proceedings of the fifth annual workshop on
Computational learning theory, pages 144–152, New York, NY, USA. ACM. 18, 141
Bottou, L. (1998). Online algorithms and stochastic approximations. In D. Saad, editor,
Online Learning in Neural Networks. Cambridge University Press, Cambridge, UK. 296
Bottou, L. (2011). From machine learning to machine reasoning. Technical report,
arXiv.1102.1808. 401
Bottou, L. (2015). Multilayer neural networks. Deep Learning Summer School. 440
Bottou, L. and Bousquet, O. (2008). The tradeoffs of large scale learning. In NIPS’2008 .
282, 295
Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. (2012). Modeling temporal
dependencies in high-dimensional sequences: Application to polyphonic music generation
and transcription. In ICML’12 . 685, 686
Boureau, Y., Ponce, J., and LeCun, Y. (2010). A theoretical analysis of feature pooling in
vision algorithms. In Proc. International Conference on Machine learning (ICML’10).
Boureau, Y., Le Roux, N., Bach, F., Ponce, J., and LeCun, Y. (2011). Ask the locals:
multi-way local pooling for image recognition. In Proc. International Conference on
Computer Vision (ICCV’11). IEEE. 345
Bourlard, H. and Kamp, Y. (1988). Auto-association by multilayer perceptrons and
singular value decomposition. Biological Cybernetics, 59, 291–294. 502
Bourlard, H. and Wellekens, C. (1989). Speech pattern discrimination and multi-layered
perceptrons. Computer Speech and Language, 3, 1–19. 459
Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University
Press, New York, NY, USA. 93
Brady, M. L., Raghavan, R., and Slawny, J. (1989). Back-propagation fails to separate
where perceptrons succeed. IEEE Transactions on Circuits and Systems,
, 665–674.
Brakel, P., Stroobandt, D., and Schrauwen, B. (2013). Training energy-based models for
time-series imputation. Journal of Machine Learning Research,
, 2771–2797. 674,
Brand, M. (2003). Charting a manifold. In NIPS’2002 , pages 961–968. MIT Press. 164,
Breiman, L. (1994). Bagging predictors. Machine Learning, 24(2), 123–140. 256
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and
Regression Trees. Wadsworth International Group, Belmont, CA. 146
Bridle, J. S. (1990). Alphanets: a recurrent ‘neural’ network architecture with a hidden
Markov model interpretation. Speech Communication, 9(1), 83–92. 186
Briggman, K., Denk, W., Seung, S., Helmstaedter, M. N., and Turaga, S. C. (2009).
Maximin affinity learning of image segmentation. In NIPS’2009 , pages 1865–1873. 360
Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D.,
Mercer, R. L., and Roossin, P. S. (1990). A statistical approach to machine translation.
Computational linguistics, 16(2), 79–85. 21
Brown, P. F., Pietra, V. J. D., DeSouza, P. V., Lai, J. C., and Mercer, R. L. (1992). Class-
based n-gram models of natural language. Computational Linguistics,
, 467–479.
Bryson, A. and Ho, Y. (1969). Applied optimal control: optimization, estimation, and
control. Blaisdell Pub. Co. 225
Bryson, Jr., A. E. and Denham, W. F. (1961). A steepest-ascent method for solving
optimum programming problems. Technical Report BR-1303, Raytheon Company,
Missle and Space Division. 225
Buciluˇa, C., Caruana, R., and Niculescu-Mizil, A. (2006). Model compression. In
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery
and data mining, pages 535–541. ACM. 448
Burda, Y., Grosse, R., and Salakhutdinov, R. (2015). Importance weighted autoencoders.
arXiv preprint arXiv:1509.00519 . 698
Cai, M., Shi, Y., and Liu, J. (2013). Deep maxout neural networks for speech recognition.
In Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop
on, pages 291–296. IEEE. 194
Carreira-Perpiñan, M. A. and Hinton, G. E. (2005). On contrastive divergence learning.
In R. G. Cowell and Z. Ghahramani, editors, Proceedings of the Tenth International
Workshop on Artificial Intelligence and Statistics (AISTATS’05), pages 33–40. Society
for Artificial Intelligence and Statistics. 611
Caruana, R. (1993). Multitask connectionist learning. In Proc. 1993 Connectionist Models
Summer School, pages 372–379. 244
Cauchy, A. (1847). Méthode générale pour la résolution de systèmes d’équations simul-
tanées. In Compte rendu des ances de l’académie des sciences, pages 536–538. 83,
Cayton, L. (2005). Algorithms for manifold learning. Technical Report CS2008-0923,
UCSD. 164
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM
computing surveys (CSUR), 41(3), 15. 102
Chapelle, O., Weston, J., and Schölkopf, B. (2003). Cluster kernels for semi-supervised
learning. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural
Information Processing Systems 15 (NIPS’02), pages 585–592, Cambridge, MA. MIT
Press. 244
Chapelle, O., Schölkopf, B., and Zien, A., editors (2006). Semi-Supervised Learning. MIT
Press, Cambridge, MA. 244, 541
Chellapilla, K., Puri, S., and Simard, P. (2006). High Performance Convolutional Neural
Networks for Document Processing. In Guy Lorette, editor, Tenth International
Workshop on Frontiers in Handwriting Recognition, La Baule (France). Université de
Rennes 1, Suvisoft. 24, 27, 445
Chen, B., Ting, J.-A., Marlin, B. M., and de Freitas, N. (2010). Deep learning of invariant
spatio-temporal features from video. NIPS*2010 Deep Learning and Unsupervised
Feature Learning Workshop. 360
Chen, S. F. and Goodman, J. T. (1999). An empirical study of smoothing techniques for
language modeling. Computer, Speech and Language, 13(4), 359–393. 462, 463, 473
Chen, T., Du, Z., Sun, N., Wang, J., Wu, C., Chen, Y., and Temam, O. (2014a). DianNao:
A small-footprint high-throughput accelerator for ubiquitous machine-learning. In Pro-
ceedings of the 19th international conference on Architectural support for programming
languages and operating systems, pages 269–284. ACM. 451
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C.,
and Zhang, Z. (2015). MXNet: A flexible and efficient machine learning library for
heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 . 25
Chen, Y., Luo, T., Liu, S., Zhang, S., He, L., Wang, J., Li, L., Chen, T., Xu, Z., Sun, N.,
et al. (2014b). DaDianNao: A machine-learning supercomputer. In Microarchitecture
(MICRO), 2014 47th Annual IEEE/ACM International Symposium on, pages 609–622.
IEEE. 451
Chilimbi, T., Suzue, Y., Apacible, J., and Kalyanaraman, K. (2014). Project Adam:
Building an efficient and scalable deep learning training system. In 11th USENIX
Symposium on Operating Systems Design and Implementation (OSDI’14). 447
Cho, K., Raiko, T., and Ilin, A. (2010). Parallel tempering is efficient for learning restricted
Boltzmann machines. In IJCNN’2010 . 603, 614
Cho, K., Raiko, T., and Ilin, A. (2011). Enhanced gradient and adaptive learning rate for
training restricted Boltzmann machines. In ICML’2011 , pages 105–112. 674
Cho, K., van Merriënboer, B., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y.
(2014a). Learning phrase representations using RNN encoder-decoder for statistical
machine translation. In Proceedings of the Empiricial Methods in Natural Language
Processing (EMNLP 2014). 397, 474, 475
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014b). On the prop-
erties of neural machine translation: Encoder-decoder approaches. ArXiv e-prints,
abs/1409.1259. 412
Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., and LeCun, Y. (2014). The
loss surface of multilayer networks. 285, 286
Chorowski, J., Bahdanau, D., Cho, K., and Bengio, Y. (2014). End-to-end continuous
speech recognition using attention-based recurrent NN: First results. arXiv:1412.1602.
Christianson, B. (1992). Automatic Hessians by reverse accumulation. IMA Journal of
Numerical Analysis, 12(2), 135–150. 224
Chrupala, G., Kadar, A., and Alishahi, A. (2015). Learning language through pictures.
arXiv 1506.03694. 412
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated
recurrent neural networks on sequence modeling. NIPS’2014 Deep Learning workshop,
arXiv 1412.3555. 412, 460
Chung, J., Gülçehre, Ç., Cho, K., and Bengio, Y. (2015a). Gated feedback recurrent
neural networks. In ICML’15 . 412
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A., and Bengio, Y. (2015b). A
recurrent latent variable model for sequential data. In NIPS’2015 . 698
Ciresan, D., Meier, U., Masci, J., and Schmidhuber, J. (2012). Multi-column deep neural
network for traffic sign classification. Neural Networks, 32, 333–338. 23, 201
Ciresan, D. C., Meier, U., Gambardella, L. M., and Schmidhuber, J. (2010). Deep big
simple neural nets for handwritten digit recognition. Neural Computation,
, 1–14.
24, 27, 446
Coates, A. and Ng, A. Y. (2011). The importance of encoding versus training with sparse
coding and vector quantization. In ICML’2011 . 27, 256, 498
Coates, A., Lee, H., and Ng, A. Y. (2011). An analysis of single-layer networks in
unsupervised feature learning. In Proceedings of the Thirteenth International Conference
on Artificial Intelligence and Statistics (AISTATS 2011). 363, 364, 455
Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., and Andrew, N. (2013).
Deep learning with COTS HPC systems. In S. Dasgupta and D. McAllester, editors,
Proceedings of the 30th International Conference on Machine Learning (ICML-13),
volume 28 (3), pages 1337–1345. JMLR Workshop and Conference Proceedings. 24, 27,
364, 447
Cohen, N., Sharir, O., and Shashua, A. (2015). On the expressive power of deep learning:
A tensor analysis. arXiv:1509.05009. 554
Collobert, R. (2004). Large Scale Machine Learning. Ph.D. thesis, Université de Paris VI,
LIP6. 197
Collobert, R. (2011). Deep learning for efficient discriminative parsing. In AISTATS’2011 .
101, 477
Collobert, R. and Weston, J. (2008a). A unified architecture for natural language processing:
Deep neural networks with multitask learning. In ICML’2008 . 471, 477
Collobert, R. and Weston, J. (2008b). A unified architecture for natural language
processing: Deep neural networks with multitask learning. In ICML’2008 . 535
Collobert, R., Bengio, S., and Bengio, Y. (2001). A parallel mixture of SVMs for very
large scale problems. Technical Report IDIAP-RR-01-12, IDIAP. 450
Collobert, R., Bengio, S., and Bengio, Y. (2002). Parallel mixture of SVMs for very large
scale problems. Neural Computation, 14(5), 1105–1114. 450
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa, P. (2011a).
Natural language processing (almost) from scratch. The Journal of Machine Learning
Research, 12, 2493–2537. 328, 477, 535, 536
Collobert, R., Kavukcuoglu, K., and Farabet, C. (2011b). Torch7: A Matlab-like environ-
ment for machine learning. In BigLearn, NIPS Workshop. 25, 214, 446
Comon, P. (1994). Independent component analysis - a new concept? Signal Processing,
36, 287–314. 491
Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning,
273–297. 18, 141
Couprie, C., Farabet, C., Najman, L., and LeCun, Y. (2013). Indoor semantic segmentation
using depth information. In International Conference on Learning Representations
(ICLR2013). 23, 201
Courbariaux, M., Bengio, Y., and David, J.-P. (2015). Low precision arithmetic for deep
learning. In Arxiv:1412.7024, ICLR’2015 Workshop. 452
Courville, A., Bergstra, J., and Bengio, Y. (2011). Unsupervised models of images by
spike-and-slab RBMs. In ICML’11 . 561, 681
Courville, A., Desjardins, G., Bergstra, J., and Bengio, Y. (2014). The spike-and-slab
RBM and extensions to discrete and sparse data distributions. Pattern Analysis and
Machine Intelligence, IEEE Transactions on, 36(9), 1874–1887. 682
Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory, 2nd Edition.
Wiley-Interscience. 73
Cox, D. and Pinto, N. (2011). Beyond simple features: A large-scale feature search
approach to unconstrained face recognition. In Automatic Face & Gesture Recognition
and Workshops (FG 2011), 2011 IEEE International Conference on, pages 8–15. IEEE.
Cramér, H. (1946). Mathematical methods of statistics. Princeton University Press. 135,
Crick, F. H. C. and Mitchison, G. (1983). The function of dream sleep. Nature,
111–114. 609
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics
of Control, Signals, and Systems, 2, 303–314. 198
Dahl, G. E., Ranzato, M., Mohamed, A., and Hinton, G. E. (2010). Phone recognition
with the mean-covariance restricted Boltzmann machine. In NIPS’2010 . 23
Dahl, G. E., Yu, D., Deng, L., and Acero, A. (2012). Context-dependent pre-trained deep
neural networks for large vocabulary speech recognition. IEEE Transactions on Audio,
Speech, and Language Processing, 20(1), 33–42. 459
Dahl, G. E., Sainath, T. N., and Hinton, G. E. (2013). Improving deep neural networks
for LVCSR using rectified linear units and dropout. In ICASSP’2013 . 460
Dahl, G. E., Jaitly, N., and Salakhutdinov, R. (2014). Multi-task neural networks for
QSAR predictions. arXiv:1406.1231. 26
Dauphin, Y. and Bengio, Y. (2013). Stochastic ratio matching of RBMs for sparse
high-dimensional inputs. In NIPS26 . NIPS Foundation. 619
Dauphin, Y., Glorot, X., and Bengio, Y. (2011). Large-scale learning of embeddings with
reconstruction sampling. In ICML’2011 . 471
Dauphin, Y., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., and Bengio, Y. (2014).
Identifying and attacking the saddle point problem in high-dimensional non-convex
optimization. In NIPS’2014 . 285, 286, 288
Davis, A., Rubinstein, M., Wadhwa, N., Mysore, G., Durand, F., and Freeman, W. T.
(2014). The visual microphone: Passive recovery of sound from video. ACM Transactions
on Graphics (Proc. SIGGRAPH), 33(4), 79:1–79:10. 452
Dayan, P. (1990). Reinforcement comparison. In Connectionist Models: Proceedings of
the 1990 Connectionist Summer School , San Mateo, CA. 691
Dayan, P. and Hinton, G. E. (1996). Varieties of Helmholtz machine. Neural Networks,
9(8), 1385–1403. 693
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S. (1995). The Helmholtz machine.
Neural computation, 7(5), 889–904. 693
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q., Mao, M., Ranzato, M.,
Senior, A., Tucker, P., Yang, K., and Ng, A. Y. (2012). Large scale distributed deep
networks. In NIPS’2012 . 25, 447
Dean, T. and Kanazawa, K. (1989). A model for reasoning about persistence and causation.
Computational Intelligence, 5(3), 142–150. 662
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990).
Indexing by latent semantic analysis. Journal of the American Society for Information
Science, 41(6), 391–407. 477, 482
Delalleau, O. and Bengio, Y. (2011). Shallow vs. deep sum-product networks. In NIPS.
19, 554
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A
Large-Scale Hierarchical Image Database. In CVPR09 . 21
Deng, J., Berg, A. C., Li, K., and Fei-Fei, L. (2010a). What does classifying more than
10,000 image categories tell us? In Proceedings of the 11th European Conference on
Computer Vision: Part V , ECCV’10, pages 71–84, Berlin, Heidelberg. Springer-Verlag.
Deng, L. and Yu, D. (2014). Deep learning methods and applications. Foundations and
Trends in Signal Processing. 460
Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed, A., and Hinton, G. (2010b). Binary
coding of speech spectrograms using a deep auto-encoder. In Interspeech 2010 , Makuhari,
Chiba, Japan. 23
Denil, M., Bazzani, L., Larochelle, H., and de Freitas, N. (2012). Learning where to attend
with deep architectures for image tracking. Neural Computation,
(8), 2151–2184. 367
Denton, E., Chintala, S., Szlam, A., and Fergus, R. (2015). Deep generative image models
using a Laplacian pyramid of adversarial networks. NIPS . 702, 719
Desjardins, G. and Bengio, Y. (2008). Empirical evaluation of convolutional RBMs for
vision. Technical Report 1327, Département d’Informatique et de Recherche Opéra-
tionnelle, Université de Montréal. 683
Desjardins, G., Courville, A. C., Bengio, Y., Vincent, P., and Delalleau, O. (2010).
Tempered Markov chain Monte Carlo for training of restricted Boltzmann machines. In
International Conference on Artificial Intelligence and Statistics, pages 145–152. 603,
Desjardins, G., Courville, A., and Bengio, Y. (2011). On tracking the partition function.
In NIPS’2011 . 629
Desjardins, G., Simonyan, K., Pascanu, R., et al. (2015). Natural neural networks. In
Advances in Neural Information Processing Systems, pages 2062–2070. 320
Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R., and Makhoul, J. (2014). Fast
and robust neural network joint models for statistical machine translation. In Proc.
ACL’2014 . 473
Devroye, L. (2013). Non-Uniform Random Variate Generation. SpringerLink : Bücher.
Springer New York. 694
DiCarlo, J. J. (2013). Mechanisms underlying visual object recognition: Humans vs.
neurons vs. machines. NIPS Tutorial. 26, 366
Dinh, L., Krueger, D., and Bengio, Y. (2014). NICE: Non-linear independent components
estimation. arXiv:1410.8516. 493
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko,
K., and Darrell, T. (2014). Long-term recurrent convolutional networks for visual
recognition and description. arXiv:1411.4389. 102
Donoho, D. L. and Grimes, C. (2003). Hessian eigenmaps: new locally linear embedding
techniques for high-dimensional data. Technical Report 2003-08, Dept. Statistics,
Stanford University. 164, 519
Dosovitskiy, A., Springenberg, J. T., and Brox, T. (2015). Learning to generate chairs with
convolutional neural networks. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, pages 1538–1546. 696, 704, 705
Doya, K. (1993). Bifurcations of recurrent neural networks in gradient descent learning.
IEEE Transactions on Neural Networks, 1, 75–80. 401, 403
Dreyfus, S. E. (1962). The numerical solution of variational problems. Journal of
Mathematical Analysis and Applications, 5(1), 30–45. 225
Dreyfus, S. E. (1973). The computational solution of optimal control problems with time
lag. IEEE Transactions on Automatic Control, 18(4), 383–385. 225
Drucker, H. and LeCun, Y. (1992). Improving generalisation performance using double
back-propagation. IEEE Transactions on Neural Networks, 3(6), 991–997. 271
Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online
learning and stochastic optimization. Journal of Machine Learning Research. 307
Dudik, M., Langford, J., and Li, L. (2011). Doubly robust policy evaluation and learning.
In Proceedings of the 28th International Conference on Machine learning, ICML ’11.
Dugas, C., Bengio, Y., Bélisle, F., and Nadeau, C. (2001). Incorporating second-order
functional knowledge for better option pricing. In T. Leen, T. Dietterich, and V. Tresp,
editors, Advances in Neural Information Processing Systems 13 (NIPS’00), pages
472–478. MIT Press. 68, 197
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015). Training generative neural net-
works via maximum mean discrepancy optimization. arXiv preprint arXiv:1505.03906 .
El Hihi, S. and Bengio, Y. (1996). Hierarchical recurrent neural networks for long-term
dependencies. In NIPS’1995 . 398, 407, 408
Elkahky, A. M., Song, Y., and He, X. (2015). A multi-view deep learning approach for
cross domain user modeling in recommendation systems. In Proceedings of the 24th
International Conference on World Wide Web, pages 278–288. 480
Elman, J. L. (1993). Learning and development in neural networks: The importance of
starting small. Cognition, 48, 781–799. 328
Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., and Vincent, P. (2009). The difficulty
of training deep architectures and the effect of unsupervised pre-training. In Proceedings
of AISTATS’2009 . 201
Erhan, D., Bengio, Y., Courville, A., Manzagol, P., Vincent, P., and Bengio, S. (2010).
Why does unsupervised pre-training help deep learning? J. Machine Learning Res.
529, 533, 534
Fahlman, S. E., Hinton, G. E., and Sejnowski, T. J. (1983). Massively parallel architectures
for AI: NETL, thistle, and Boltzmann machines. In Proceedings of the National
Conference on Artificial Intelligence AAAI-83 . 570, 654
Fang, H., Gupta, S., Iandola, F., Srivastava, R., Deng, L., Dollár, P., Gao, J., He, X.,
Mitchell, M., Platt, J. C., Zitnick, C. L., and Zweig, G. (2015). From captions to visual
concepts and back. arXiv:1411.4952. 102
Farabet, C., LeCun, Y., Kavukcuoglu, K., Culurciello, E., Martini, B., Akselrod, P., and
Talay, S. (2011). Large-scale FPGA-based convolutional networks. In R. Bekkerman,
M. Bilenko, and J. Langford, editors, Scaling up Machine Learning: Parallel and
Distributed Approaches. Cambridge University Press. 523
Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (2013). Learning hierarchical features
for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence,
35(8), 1915–1929. 23, 201, 360
Fei-Fei, L., Fergus, R., and Perona, P. (2006). One-shot learning of object categories.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
(4), 594–611. 538
Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., and Abbeel, P. (2015). Learning
visual feature spaces for robotic manipulation with deep spatial autoencoders. arXiv
preprint arXiv:1509.06113 . 25
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals
of Eugenics, 7, 179–188. 21, 105
Földiák, P. (1989). Adaptive network for optimal linear feature extraction. In International
Joint Conference on Neural Networks (IJCNN), volume 1, pages 401–405, Washington
1989. IEEE, New York. 494
Franzius, M., Sprekeler, H., and Wiskott, L. (2007). Slowness and sparseness lead to place,
head-direction, and spatial-view cells. 495
Franzius, M., Wilbert, N., and Wiskott, L. (2008). Invariant object recognition with slow
feature analysis. In Artificial Neural Networks-ICANN 2008 , pages 961–970. Springer.
Frasconi, P., Gori, M., and Sperduti, A. (1997). On the efficient classification of data
structures by neural networks. In Proc. Int. Joint Conf. on Artificial Intelligence. 401
Frasconi, P., Gori, M., and Sperduti, A. (1998). A general framework for adaptive
processing of data structures. IEEE Transactions on Neural Networks,
(5), 768–786.
Freund, Y. and Schapire, R. E. (1996a). Experiments with a new boosting algorithm. In
Machine Learning: Proceedings of Thirteenth International Conference, pages 148–156,
USA. ACM. 258
Freund, Y. and Schapire, R. E. (1996b). Game theory, on-line prediction and boosting. In
Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages
325–332. 258
Frey, B. J. (1998). Graphical models for machine learning and digital communication.
MIT Press. 705, 706
Frey, B. J., Hinton, G. E., and Dayan, P. (1996). Does the wake-sleep algorithm learn good
density estimators? In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances
in Neural Information Processing Systems 8 (NIPS’95), pages 661–670. MIT Press,
Cambridge, MA. 651
Frobenius, G. (1908). Über matrizen aus positiven elementen, s. B. Preuss. Akad. Wiss.
Berlin, Germany. 597
Fukushima, K. (1975). Cognitron: A self-organizing multilayered neural network. Biological
Cybernetics, 20, 121–136. 16, 226, 528
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a
mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics,
36, 193–202. 16, 24, 27, 226, 367
Gal, Y. and Ghahramani, Z. (2015). Bayesian convolutional neural networks with Bernoulli
approximate variational inference. arXiv preprint arXiv:1506.02158 . 264
Gallinari, P., LeCun, Y., Thiria, S., and Fogelman-Soulie, F. (1987). Memoires associatives
distribuees. In Proceedings of COGNITIVA 87 , Paris, La Villette. 515
Garcia-Duran, A., Bordes, A., Usunier, N., and Grandvalet, Y. (2015). Combining two
and three-way embeddings models for link prediction in knowledge bases. arXiv preprint
arXiv:1506.00999 . 484
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., and Pallett, D. S. (1993).
Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1.
NASA STI/Recon Technical Report N , 93, 27403. 459
Garson, J. (1900). The metric system of identification of criminals, as used in Great
Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and
Ireland, (2), 177–227. 21
Gers, F. A., Schmidhuber, J., and Cummins, F. (2000). Learning to forget: Continual
prediction with LSTM. Neural computation, 12(10), 2451–2471. 410, 412
Ghahramani, Z. and Hinton, G. E. (1996). The EM algorithm for mixtures of factor
analyzers. Technical Report CRG-TR-96-1, Dpt. of Comp. Sci., Univ. of Toronto. 489
Gillick, D., Brunk, C., Vinyals, O., and Subramanya, A. (2015). Multilingual language
processing from bytes. arXiv preprint arXiv:1512.00103 . 477
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2015). Region-based convolutional
networks for accurate object detection and segmentation. 426
Giudice, M. D., Manera, V., and Keysers, C. (2009). Programmed to learn? The ontogeny
of mirror neurons. Dev. Sci., 12(2), 350––363. 656
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward
neural networks. In AISTATS’2010 . 303
Glorot, X., Bordes, A., and Bengio, Y. (2011a). Deep sparse rectifier neural networks. In
AISTATS’2011 . 16, 174, 197, 226, 227
Glorot, X., Bordes, A., and Bengio, Y. (2011b). Domain adaptation for large-scale
sentiment classification: A deep learning approach. In ICML’2011 . 507, 537
Goldberger, J., Roweis, S., Hinton, G. E., and Salakhutdinov, R. (2005). Neighbourhood
components analysis. In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural
Information Processing Systems 17 (NIPS’04). MIT Press. 115
Gong, S., McKenna, S., and Psarrou, A. (2000). Dynamic Vision: From Images to Face
Recognition. Imperial College Press. 165, 519
Goodfellow, I., Le, Q., Saxe, A., and Ng, A. (2009). Measuring invariances in deep
networks. In NIPS’2009 , pages 646–654. 255
Goodfellow, I., Koenig, N., Muja, M., Pantofaru, C., Sorokin, A., and Takayama, L. (2010).
Help me help you: Interfaces for personal robots. In Proc. of Human Robot Interaction
(HRI), Osaka, Japan. ACM Press, ACM Press. 100
Goodfellow, I. J. (2010). Technical report: Multidimensional, downsampled convolution
for autoencoders. Technical report, Université de Montréal. 357
Goodfellow, I. J. (2014). On distinguishability criteria for estimating generative models.
In International Conference on Learning Representations, Workshops Track . 622, 700,
Goodfellow, I. J., Courville, A., and Bengio, Y. (2011). Spike-and-slab sparse coding
for unsupervised feature discovery. In NIPS Workshop on Challenges in Learning
Hierarchical Models. 532, 538
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013a).
Maxout networks. In S. Dasgupta and D. McAllester, editors, ICML’13 , pages 1319–
1327. 193, 264, 344, 365, 455
Goodfellow, I. J., Mirza, M., Courville, A., and Bengio, Y. (2013b). Multi-prediction deep
Boltzmann machines. In NIPS26 . NIPS Foundation. 100, 617, 671, 672, 673, 674, 675,
Goodfellow, I. J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R.,
Bergstra, J., Bastien, F., and Bengio, Y. (2013c). Pylearn2: a machine learning research
library. arXiv preprint arXiv:1308.4214 . 25, 446
Goodfellow, I. J., Courville, A., and Bengio, Y. (2013d). Scaling up spike-and-slab models
for unsupervised feature learning. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 35(8), 1902–1914. 497, 498, 499, 650, 683
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y. (2014a). An empirical
investigation of catastrophic forgeting in gradient-based neural networks. In ICLR’2014 .
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014b). Explaining and harnessing adver-
sarial examples. CoRR, abs/1412.6572. 268, 269, 271, 555, 556
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S.,
Courville, A., and Bengio, Y. (2014c). Generative adversarial networks. In NIPS’2014 .
544, 689, 699, 701, 704
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V. (2014d). Multi-digit
number recognition from Street View imagery using deep convolutional neural networks.
In International Conference on Learning Representations. 25, 101, 201, 202, 203, 391,
422, 449
Goodfellow, I. J., Vinyals, O., and Saxe, A. M. (2015). Qualitatively characterizing neural
network optimization problems. In International Conference on Learning Representa-
tions. 285, 286, 287, 291
Goodman, J. (2001). Classes for fast maximum entropy training. In International
Conference on Acoustics, Speech and Signal Processing (ICASSP), Utah. 467
Gori, M. and Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
(1), 76–86. 284
Gosset, W. S. (1908). The probable error of a mean. Biometrika,
(1), 1–25. Originally
published under the pseudonym “Student”. 21
Gouws, S., Bengio, Y., and Corrado, G. (2014). BilBOWA: Fast bilingual distributed
representations without word alignments. Technical report, arXiv:1410.2455. 476, 539
Graf, H. P. and Jackel, L. D. (1989). Analog electronic neural network circuits. Circuits
and Devices Magazine, IEEE , 5(4), 44–49. 451
Graves, A. (2011). Practical variational inference for neural networks. In NIPS’2011 . 242
Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks. Studies
in Computational Intelligence. Springer. 374, 395, 411, 460
Graves, A. (2013). Generating sequences with recurrent neural networks. Technical report,
arXiv:1308.0850. 190, 410, 415, 420
Graves, A. and Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent
neural networks. In ICML’2014 . 410
Graves, A. and Schmidhuber, J. (2005). Framewise phoneme classification with bidirec-
tional LSTM and other neural network architectures. Neural Networks,
(5), 602–610.
Graves, A. and Schmidhuber, J. (2009). Offline handwriting recognition with multidi-
mensional recurrent neural networks. In D. Koller, D. Schuurmans, Y. Bengio, and
L. Bottou, editors, NIPS’2008 , pages 545–552. 395
Graves, A., Fernández, S., Gomez, F., and Schmidhuber, J. (2006). Connectionist temporal
classification: Labelling unsegmented sequence data with recurrent neural networks. In
ICML’2006 , pages 369–376, Pittsburgh, USA. 460
Graves, A., Liwicki, M., Bunke, H., Schmidhuber, J., and Fernández, S. (2008). Uncon-
strained on-line handwriting recognition with recurrent neural networks. In J. Platt,
D. Koller, Y. Singer, and S. Roweis, editors, NIPS’2007 , pages 577–584. 395
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., and Schmidhuber, J.
(2009). A novel connectionist system for unconstrained handwriting recognition. Pattern
Analysis and Machine Intelligence, IEEE Transactions on, 31(5), 855–868. 410
Graves, A., Mohamed, A., and Hinton, G. (2013). Speech recognition with deep recurrent
neural networks. In ICASSP’2013 , pages 6645–6649. 395, 398, 410, 411, 460
Graves, A., Wayne, G., and Danihelka, I. (2014a). Neural Turing machines.
arXiv:1410.5401. 25
Graves, A., Wayne, G., and Danihelka, I. (2014b). Neural Turing machines. arXiv preprint
arXiv:1410.5401 . 418
Grefenstette, E., Hermann, K. M., Suleyman, M., and Blunsom, P. (2015). Learning to
transduce with unbounded memory. In NIPS’2015 . 418
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and Schmidhuber, J. (2015).
LSTM: a search space odyssey. arXiv preprint arXiv:1503.04069 . 412
Gregor, K. and LeCun, Y. (2010a). Emergence of complex-like cells in a temporal product
network with local receptive fields. Technical report, arXiv:1006.0448. 352
Gregor, K. and LeCun, Y. (2010b). Learning fast approximations of sparse coding. In
L. Bottou and M. Littman, editors, Proceedings of the Twenty-seventh International
Conference on Machine Learning (ICML-10). ACM. 652
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D. (2014). Deep
autoregressive networks. In International Conference on Machine Learning (ICML’2014).
Gregor, K., Danihelka, I., Graves, A., and Wierstra, D. (2015). DRAW: A recurrent neural
network for image generation. arXiv preprint arXiv:1502.04623 . 698
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012). A
kernel two-sample test. The Journal of Machine Learning Research,
(1), 723–773.
Gülçehre, Ç. and Bengio, Y. (2013). Knowledge matters: Importance of prior information
for optimization. In International Conference on Learning Representations (ICLR’2013).
Guo, H. and Gelfand, S. B. (1992). Classification trees with neural network feature
extraction. Neural Networks, IEEE Transactions on, 3(6), 923–933. 450
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P. (2015). Deep learning
with limited numerical precision. CoRR, abs/1502.02551. 452
Gutmann, M. and Hyvarinen, A. (2010). Noise-contrastive estimation: A new estima-
tion principle for unnormalized statistical models. In Proceedings of The Thirteenth
International Conference on Artificial Intelligence and Statistics (AISTATS’10). 620
Hadsell, R., Sermanet, P., Ben, J., Erkan, A., Han, J., Muller, U., and LeCun, Y.
(2007). Online learning for offroad robots: Spatial label propagation to learn long-range
traversability. In Proceedings of Robotics: Science and Systems, Atlanta, GA, USA. 453
Hajnal, A., Maass, W., Pudlak, P., Szegedy, M., and Turan, G. (1993). Threshold circuits
of bounded depth. J. Comput. System. Sci., 46, 129–154. 199
Håstad, J. (1986). Almost optimal lower bounds for small depth circuits. In Proceedings
of the 18th annual ACM Symposium on Theory of Computing, pages 6–20, Berkeley,
California. ACM Press. 199
Håstad, J. and Goldmann, M. (1991). On the power of small-depth threshold circuits.
Computational Complexity, 1, 113–129. 199
Hastie, T., Tibshirani, R., and Friedman, J. (2001). The elements of statistical learning:
data mining, inference and prediction. Springer Series in Statistics. Springer Verlag.
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification. arXiv preprint arXiv:1502.01852 .
28, 193
Hebb, D. O. (1949). The Organization of Behavior. Wiley, New York. 14, 17, 656
Henaff, M., Jarrett, K., Kavukcuoglu, K., and LeCun, Y. (2011). Unsupervised learning
of sparse features for scalable audio classification. In ISMIR’11 . 523
Henderson, J. (2003). Inducing history representations for broad coverage statistical
parsing. In HLT-NAACL, pages 103–110. 477
Henderson, J. (2004). Discriminative training of a neural network statistical parser. In
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics,
page 95. 477
Henniges, M., Puertas, G., Bornschein, J., Eggert, J., and Lücke, J. (2010). Binary sparse
coding. In Latent Variable Analysis and Signal Separation, pages 450–457. Springer.
Herault, J. and Ans, B. (1984). Circuits neuronaux à synapses modifiables: Décodage de
messages composites par apprentissage non supervisé. Comptes Rendus de l’Académie
des Sciences, 299(III-13), 525––528. 491
Hinton, G. (2012). Neural networks for machine learning. Coursera, video lectures. 307
Hinton, G., Deng, L., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V.,
Nguyen, P., Sainath, T., and Kingsbury, B. (2012a). Deep neural networks for acoustic
modeling in speech recognition. IEEE Signal Processing Magazine,
(6), 82–97. 23,
Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network.
arXiv preprint arXiv:1503.02531 . 448
Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence,
185–234. 494
Hinton, G. E. (1990). Mapping part-whole hierarchies into connectionist networks. Artificial
Intelligence, 46(1), 47–75. 418
Hinton, G. E. (1999). Products of experts. In ICANN’1999 . 571
Hinton, G. E. (2000). Training products of experts by minimizing contrastive divergence.
Technical Report GCNU TR 2000-004, Gatsby Unit, University College London. 610,
Hinton, G. E. (2006). To recognize shapes, first learn to generate images. Technical Report
UTML TR 2006-003, University of Toronto. 528, 595
Hinton, G. E. (2007a). How to do backpropagation in a brain. Invited talk at the
NIPS’2007 Deep Learning Workshop. 656
Hinton, G. E. (2007b). Learning multiple layers of representation. Trends in cognitive
sciences, 11(10), 428–434. 660
Hinton, G. E. (2010). A practical guide to training restricted Boltzmann machines.
Technical Report UTML TR 2010-003, Department of Computer Science, University of
Toronto. 610
Hinton, G. E. and Ghahramani, Z. (1997). Generative models for discovering sparse
distributed representations. Philosophical Transactions of the Royal Society of London.
Hinton, G. E. and McClelland, J. L. (1988). Learning representations by recirculation. In
NIPS’1987 , pages 358–366. 502
Hinton, G. E. and Roweis, S. (2003). Stochastic neighbor embedding. In NIPS’2002 . 519
Hinton, G. E. and Salakhutdinov, R. (2006). Reducing the dimensionality of data with
neural networks. Science, 313(5786), 504–507. 509, 524, 528, 529, 534
Hinton, G. E. and Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines.
In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing,
volume 1, chapter 7, pages 282–317. MIT Press, Cambridge. 570, 654
Hinton, G. E. and Sejnowski, T. J. (1999). Unsupervised learning: foundations of neural
computation. MIT press. 541
Hinton, G. E. and Shallice, T. (1991). Lesioning an attractor network: investigations of
acquired dyslexia. Psychological review, 98(1), 74. 13
Hinton, G. E. and Zemel, R. S. (1994). Autoencoders, minimum description length, and
Helmholtz free energy. In NIPS’1993 . 502
Hinton, G. E., Sejnowski, T. J., and Ackley, D. H. (1984). Boltzmann machines: Constraint
satisfaction networks that learn. Technical Report TR-CMU-CS-84-119, Carnegie-Mellon
University, Dept. of Computer Science. 570, 654
Hinton, G. E., McClelland, J., and Rumelhart, D. (1986). Distributed representations.
In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing:
Explorations in the Microstructure of Cognition, volume 1, pages 77–109. MIT Press,
Cambridge. 17, 225, 526
Hinton, G. E., Revow, M., and Dayan, P. (1995a). Recognizing handwritten digits using
mixtures of linear models. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances
in Neural Information Processing Systems 7 (NIPS’94), pages 1015–1022. MIT Press,
Cambridge, MA. 489
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995b). The wake-sleep algorithm
for unsupervised neural networks. Science, 268, 1558–1161. 504, 651
Hinton, G. E., Dayan, P., and Revow, M. (1997). Modelling the manifolds of images of
handwritten digits. IEEE Transactions on Neural Networks, 8, 65–74. 499
Hinton, G. E., Welling, M., Teh, Y. W., and Osindero, S. (2001). A new view of ICA. In
Proceedings of 3rd International Conference on Independent Component Analysis and
Blind Signal Separation (ICA’01), pages 746–751, San Diego, CA. 491
Hinton, G. E., Osindero, S., and Teh, Y. (2006). A fast learning algorithm for deep belief
nets. Neural Computation, 18, 1527–1554. 14, 19, 27, 143, 528, 529, 660, 661
Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A.,
Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B. (2012b). Deep neural
networks for acoustic modeling in speech recognition: The shared views of four research
groups. IEEE Signal Process. Mag., 29(6), 82–97. 101
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012c).
Improving neural networks by preventing co-adaptation of feature detectors. Technical
report, arXiv:1207.0580. 238, 263, 267
Hinton, G. E., Vinyals, O., and Dean, J. (2014). Dark knowledge. Invited talk at the
BayLearn Bay Area Machine Learning Symposium. 448
Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen. Diploma
thesis, T.U. München. 18, 401, 403
Hochreiter, S. and Schmidhuber, J. (1995). Simplifying neural nets by discovering flat
minima. In Advances in Neural Information Processing Systems 7 , pages 529–536. MIT
Press. 243
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation,
9(8), 1735–1780. 18, 410, 411
Hochreiter, S., Bengio, Y., and Frasconi, P. (2001). Gradient flow in recurrent nets: the
difficulty of learning long-term dependencies. In J. Kolen and S. Kremer, editors, Field
Guide to Dynamical Recurrent Networks. IEEE Press. 411
Holi, J. L. and Hwang, J.-N. (1993). Finite precision error analysis of neural network
hardware implementations. Computers, IEEE Transactions on, 42(3), 281–290. 451
Holt, J. L. and Baker, T. E. (1991). Back propagation simulations using limited preci-
sion calculations. In Neural Networks, 1991., IJCNN-91-Seattle International Joint
Conference on, volume 2, pages 121–126. IEEE. 451
Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are
universal approximators. Neural Networks, 2, 359–366. 198
Hornik, K., Stinchcombe, M., and White, H. (1990). Universal approximation of an
unknown mapping and its derivatives using multilayer feedforward networks. Neural
networks, 3(5), 551–560. 198
Hsu, F.-H. (2002). Behind Deep Blue: Building the Computer That Defeated the World
Chess Champion. Princeton University Press, Princeton, NJ, USA. 2
Huang, F. and Ogata, Y. (2002). Generalized pseudo-likelihood estimates for Markov
random fields on lattice. Annals of the Institute of Statistical Mathematics,
(1), 1–18.
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., and Heck, L. (2013). Learning deep
structured semantic models for web search using clickthrough data. In Proceedings of
the 22nd ACM international conference on Conference on information & knowledge
management, pages 2333–2338. ACM. 480
Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey
striate cortex. Journal of Physiology (London), 195, 215–243. 364
Hubel, D. H. and Wiesel, T. N. (1959). Receptive fields of single neurons in the cat’s
striate cortex. Journal of Physiology, 148, 574–591. 364
Hubel, D. H. and Wiesel, T. N. (1962). Receptive fields, binocular interaction, and
functional architecture in the cat’s visual cortex. Journal of Physiology (London),
106–154. 364
Huszar, F. (2015). How (not) to train your generative model: schedule sampling, likelihood,
adversary? arXiv:1511.05101. 698
Hutter, F., Hoos, H., and Leyton-Brown, K. (2011). Sequential model-based optimization
for general algorithm configuration. In LION-5 . Extended version as UBC Tech report
TR-2010-10. 436
Hyotyniemi, H. (1996). Turing machines are recurrent neural networks. In STeP’96 , pages
13–24. 379
Hyvärinen, A. (1999). Survey on independent component analysis. Neural Computing
Surveys, 2, 94–128. 491
Hyvärinen, A. (2005). Estimation of non-normalized statistical models using score matching.
Journal of Machine Learning Research, 6, 695–709. 513, 617
Hyvärinen, A. (2007a). Connections between score matching, contrastive divergence,
and pseudolikelihood for continuous-valued variables. IEEE Transactions on Neural
Networks, 18, 1529–1531. 618
Hyvärinen, A. (2007b). Some extensions of score matching. Computational Statistics and
Data Analysis, 51, 2499–2512. 618
Hyvärinen, A. and Hoyer, P. O. (1999). Emergence of topography and complex cell
properties from natural images using extensions of ica. In NIPS , pages 827–833. 493
Hyvärinen, A. and Pajunen, P. (1999). Nonlinear independent component analysis:
Existence and uniqueness results. Neural Networks, 12(3), 429–439. 493
Hyvärinen, A., Karhunen, J., and Oja, E. (2001a). Independent Component Analysis.
Wiley-Interscience. 491
Hyvärinen, A., Hoyer, P. O., and Inki, M. O. (2001b). Topographic independent component
analysis. Neural Computation, 13(7), 1527–1558. 493
Hyvärinen, A., Hurri, J., and Hoyer, P. O. (2009). Natural Image Statistics: A probabilistic
approach to early computational vision. Springer-Verlag. 370
Iba, Y. (2001). Extended ensemble Monte Carlo. International Journal of Modern Physics,
C12, 623–656. 603
Inayoshi, H. and Kurita, T. (2005). Improved generalization by adding both auto-
association and hidden-layer noise to neural-network-based-classifiers. IEEE Workshop
on Machine Learning for Signal Processing, pages 141—-146. 515
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training
by reducing internal covariate shift. 100, 317, 320
Jacobs, R. A. (1988). Increased rates of convergence through learning rate adaptation.
Neural networks, 1(4), 295–307. 307
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. (1991). Adaptive mixtures
of local experts. Neural Computation, 3, 79–87. 189, 450
Jaeger, H. (2003). Adaptive nonlinear system identification with echo state networks. In
Advances in Neural Information Processing Systems 15 . 404
Jaeger, H. (2007a). Discovering multiscale dynamical features with hierarchical echo state
networks. Technical report, Jacobs University. 398
Jaeger, H. (2007b). Echo state network. Scholarpedia, 2(9), 2330. 404
Jaeger, H. (2012). Long short-term memory in echo state networks: Details of a simulation
study. Technical report, Technical report, Jacobs University Bremen. 405
Jaeger, H. and Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and
saving energy in wireless communication. Science, 304(5667), 78–80. 27, 404
Jaeger, H., Lukosevicius, M., Popovici, D., and Siewert, U. (2007). Optimization and
applications of echo state networks with leaky- integrator neurons. Neural Networks,
20(3), 335–352. 407
Jain, V., Murray, J. F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K. L., Helmstaedter,
M. N., Denk, W., and Seung, H. S. (2007). Supervised learning of image restoration
with convolutional networks. In Computer Vision, 2007. ICCV 2007. IEEE 11th
International Conference on, pages 1–8. IEEE. 359
Jaitly, N. and Hinton, G. (2011). Learning a better representation of speech soundwaves
using restricted Boltzmann machines. In Acoustics, Speech and Signal Processing
(ICASSP), 2011 IEEE International Conference on, pages 5884–5887. IEEE. 458
Jaitly, N. and Hinton, G. E. (2013). Vocal tract length perturbation (VTLP) improves
speech recognition. In ICML’2013 . 241
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009). What is the best
multi-stage architecture for object recognition? In ICCV’09 . 16, 24, 27, 174, 193, 226,
363, 364, 523
Jarzynski, C. (1997). Nonequilibrium equality for free energy differences. Phys. Rev. Lett.,
78, 2690–2693. 625, 628
Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University
Press. 53
Jean, S., Cho, K., Memisevic, R., and Bengio, Y. (2014). On using very large target
vocabulary for neural machine translation. arXiv:1412.2007. 474, 475
Jelinek, F. and Mercer, R. L. (1980). Interpolated estimation of Markov source parameters
from sparse data. In E. S. Gelsema and L. N. Kanal, editors, Pattern Recognition in
Practice. North-Holland, Amsterdam. 462, 473
Jia, Y. (2013). Caffe: An open source convolutional architecture for fast feature embedding. 25, 214
Jia, Y., Huang, C., and Darrell, T. (2012). Beyond spatial pyramids: Receptive field
learning for pooled image features. In Computer Vision and Pattern Recognition
(CVPR), 2012 IEEE Conference on, pages 3370–3377. IEEE. 345
Jim, K.-C., Giles, C. L., and Horne, B. G. (1996). An analysis of noise in recurrent neural
networks: convergence and generalization. IEEE Transactions on Neural Networks,
7(6), 1424–1438. 242
Jordan, M. I. (1998). Learning in Graphical Models. Kluwer, Dordrecht, Netherlands. 18
Joulin, A. and Mikolov, T. (2015). Inferring algorithmic patterns with stack-augmented
recurrent nets. arXiv preprint arXiv:1503.01007 . 418
Jozefowicz, R., Zaremba, W., and Sutskever, I. (2015). An empirical evaluation of recurrent
network architectures. In ICML’2015 . 306, 412
Judd, J. S. (1989). Neural Network Design and the Complexity of Learning. MIT press.
Jutten, C. and Herault, J. (1991). Blind separation of sources, part I: an adaptive
algorithm based on neuromimetic architecture. Signal Processing, 24, 1–10. 491
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, c., Memisevic, R., Vincent,
P., Courville, A., Bengio, Y., Ferrari, R. C., Mirza, M., Jean, S., Carrier, P. L., Dauphin,
Y., Boulanger-Lewandowski, N., Aggarwal, A., Zumer, J., Lamblin, P., Raymond,
J.-P., Desjardins, G., Pascanu, R., Warde-Farley, D., Torabi, A., Sharma, A., Bengio,
E., Côté, M., Konda, K. R., and Wu, Z. (2013). Combining modality specific deep
neural networks for emotion recognition in video. In Proceedings of the 15th ACM on
International Conference on Multimodal Interaction. 201
Kalchbrenner, N. and Blunsom, P. (2013). Recurrent continuous translation models. In
EMNLP’2013 . 474, 475
Kalchbrenner, N., Danihelka, I., and Graves, A. (2015). Grid long short-term memory.
arXiv preprint arXiv:1507.01526 . 395
Kamyshanska, H. and Memisevic, R. (2015). The potential energy of an autoencoder.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 515
Karpathy, A. and Li, F.-F. (2015). Deep visual-semantic alignments for generating image
descriptions. In CVPR’2015 . arXiv:1412.2306. 102
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014).
Large-scale video classification with convolutional neural networks. In CVPR. 21
Karush, W. (1939). Minima of Functions of Several Variables with Inequalities as Side
Constraints. Master’s thesis, Dept. of Mathematics, Univ. of Chicago. 95
Katz, S. M. (1987). Estimation of probabilities from sparse data for the language model
component of a speech recognizer. IEEE Transactions on Acoustics, Speech, and Signal
Processing, ASSP-35(3), 400–401. 462, 473
Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2008). Fast inference in sparse coding
algorithms with applications to object recognition. Technical report, Computational and
Biological Learning Lab, Courant Institute, NYU. Tech Report CBLL-TR-2008-12-01.
Kavukcuoglu, K., Ranzato, M.-A., Fergus, R., and LeCun, Y. (2009). Learning invariant
features through topographic filter maps. In CVPR’2009 . 523
Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., and LeCun, Y.
(2010). Learning convolutional feature hierarchies for visual recognition. In NIPS’2010 .
364, 523
Kelley, H. J. (1960). Gradient theory of optimal flight paths. ARS Journal,
947–954. 225
Khan, F., Zhu, X., and Mutlu, B. (2011). How do humans teach: On curriculum learning
and teaching dimension. In Advances in Neural Information Processing Systems 24
(NIPS’11), pages 1449–1457. 328
Kim, S. K., McAfee, L. C., McMahon, P. L., and Olukotun, K. (2009). A highly scalable
restricted Boltzmann machine FPGA implementation. In Field Programmable Logic
and Applications, 2009. FPL 2009. International Conference on, pages 367–372. IEEE.
Kindermann, R. (1980). Markov Random Fields and Their Applications (Contemporary
Mathematics ; V. 1). American Mathematical Society. 566
Kingma, D. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv
preprint arXiv:1412.6980 . 308
Kingma, D. and LeCun, Y. (2010). Regularized estimation of image statistics by score
matching. In NIPS’2010 . 513, 620
Kingma, D., Rezende, D., Mohamed, S., and Welling, M. (2014). Semi-supervised learning
with deep generative models. In NIPS’2014 . 426
Kingma, D. P. (2013). Fast gradient-based inference with continuous latent variable
models in auxiliary form. Technical report, arxiv:1306.0733. 652, 689, 696
Kingma, D. P. and Welling, M. (2014a). Auto-encoding variational bayes. In Proceedings
of the International Conference on Learning Representations (ICLR). 689, 700
Kingma, D. P. and Welling, M. (2014b). Efficient gradient-based inference through
transformations between bayes nets and neural nets. Technical report, arxiv:1402.0480.
Kirkpatrick, S., Jr., C. D. G., , and Vecchi, M. P. (1983). Optimization by simulated
annealing. Science, 220, 671–680. 327
Kiros, R., Salakhutdinov, R., and Zemel, R. (2014a). Multimodal neural language models.
In ICML’2014 . 102
Kiros, R., Salakhutdinov, R., and Zemel, R. (2014b). Unifying visual-semantic embeddings
with multimodal neural language models. arXiv:1411.2539 [cs.LG]. 102, 410
Klementiev, A., Titov, I., and Bhattarai, B. (2012). Inducing crosslingual distributed
representations of words. In Proceedings of COLING 2012 . 476, 539
Knowles-Barley, S., Jones, T. R., Morgan, J., Lee, D., Kasthuri, N., Lichtman, J. W., and
Pfister, H. (2014). Deep learning for the connectome. GPU Technology Conference. 26
Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and
Techniques. MIT Press. 583, 595, 645
Konig, Y., Bourlard, H., and Morgan, N. (1996). REMAP: Recursive estimation and
maximization of a posteriori probabilities application to transition-based connectionist
speech recognition. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances in
Neural Information Processing Systems 8 (NIPS’95). MIT Press, Cambridge, MA. 459
Koren, Y. (2009). The BellKor solution to the Netflix grand prize. 258, 480
Kotzias, D., Denil, M., de Freitas, N., and Smyth, P. (2015). From group to individual
labels using deep features. In ACM SIGKDD. 106
Koutnik, J., Greff, K., Gomez, F., and Schmidhuber, J. (2014). A clockwork RNN. In
ICML’2014 . 408
Kočiský, T., Hermann, K. M., and Blunsom, P. (2014). Learning Bilingual Word Repre-
sentations by Marginalizing Alignments. In Proceedings of ACL. 476
Krause, O., Fischer, A., Glasmachers, T., and Igel, C. (2013). Approximation properties
of DBNs with binary hidden units and real-valued visible units. In ICML’2013 . 553
Krizhevsky, A. (2010). Convolutional deep belief networks on CIFAR-10. Technical report,
University of Toronto. Unpublished Manuscript: kriz/conv-
cifar10-aug2010.pdf. 446
Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny
images. Technical report, University of Toronto. 21, 561
Krizhevsky, A. and Hinton, G. E. (2011). Using very deep autoencoders for content-based
image retrieval. In ESANN . 525
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet classification with deep
convolutional neural networks. In NIPS’2012 . 23, 24, 27, 100, 201, 371, 454, 458
Krueger, K. A. and Dayan, P. (2009). Flexible shaping: how learning in small steps helps.
Cognition, 110, 380–394. 328
Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming. In Proceedings of the
Second Berkeley Symposium on Mathematical Statistics and Probability, pages 481–492,
Berkeley, Calif. University of California Press. 95
Kumar, A., Irsoy, O., Su, J., Bradbury, J., English, R., Pierce, B., Ondruska, P., Iyyer,
M., Gulrajani, I., and Socher, R. (2015). Ask me anything: Dynamic memory networks
for natural language processing. arXiv:1506.07285 . 418, 485
Kumar, M. P., Packer, B., and Koller, D. (2010). Self-paced learning for latent variable
models. In NIPS’2010 . 328
Lang, K. J. and Hinton, G. E. (1988). The development of the time-delay neural network
architecture for speech recognition. Technical Report CMU-CS-88-152, Carnegie-Mellon
University. 367, 374, 407
Lang, K. J., Waibel, A. H., and Hinton, G. E. (1990). A time-delay neural network
architecture for isolated word recognition. Neural networks, 3(1), 23–43. 374
Langford, J. and Zhang, T. (2008). The epoch-greedy algorithm for contextual multi-armed
bandits. In NIPS’2008 , pages 1096––1103. 480
Lappalainen, H., Giannakopoulos, X., Honkela, A., and Karhunen, J. (2000). Nonlinear
independent component analysis using ensemble learning: Experiments and discussion.
In Proc. ICA. Citeseer. 493
Larochelle, H. and Bengio, Y. (2008). Classification using discriminative restricted
Boltzmann machines. In ICML’2008 . 244, 255, 530, 686, 716
Larochelle, H. and Hinton, G. E. (2010). Learning to combine foveal glimpses with a
third-order Boltzmann machine. In Advances in Neural Information Processing Systems
23 , pages 1243–1251. 367
Larochelle, H. and Murray, I. (2011). The Neural Autoregressive Distribution Estimator.
In AISTATS’2011 . 705, 708, 709
Larochelle, H., Erhan, D., and Bengio, Y. (2008). Zero-data learning of new tasks. In
AAAI Conference on Artificial Intelligence. 539
Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P. (2009). Exploring strategies for
training deep neural networks. Journal of Machine Learning Research, 10, 1–40. 535
Lasserre, J. A., Bishop, C. M., and Minka, T. P. (2006). Principled hybrids of generative and
discriminative models. In Proceedings of the Computer Vision and Pattern Recognition
Conference (CVPR’06), pages 87–94, Washington, DC, USA. IEEE Computer Society.
244, 253
Le, Q., Ngiam, J., Chen, Z., hao Chia, D. J., Koh, P. W., and Ng, A. (2010). Tiled
convolutional neural networks. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor,
R. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems
23 (NIPS’10), pages 1279–1287. 352
Le, Q., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., and Ng, A. (2011). On optimization
methods for deep learning. In Proc. ICML’2011 . ACM. 316
Le, Q., Ranzato, M., Monga, R., Devin, M., Corrado, G., Chen, K., Dean, J., and Ng,
A. (2012). Building high-level features using large scale unsupervised learning. In
ICML’2012 . 24, 27
Le Roux, N. and Bengio, Y. (2008). Representational power of restricted Boltzmann
machines and deep belief networks. Neural Computation, 20(6), 1631–1649. 553, 655
Le Roux, N. and Bengio, Y. (2010). Deep belief networks are compact universal approxi-
mators. Neural Computation, 22(8), 2192–2207. 553
LeCun, Y. (1985). Une procédure d’apprentissage pour Réseau à seuil assymétrique. In
Cognitiva 85: A la Frontière de l’Intelligence Artificielle, des Sciences de la Connaissance
et des Neurosciences, pages 599–604, Paris 1985. CESTA, Paris. 225
LeCun, Y. (1986). Learning processes in an asymmetric threshold network. In F. Fogelman-
Soulié, E. Bienenstock, and G. Weisbuch, editors, Disordered Systems and Biological
Organization, pages 233–240. Springer-Verlag, Les Houches, France. 352
LeCun, Y. (1987). Modèles connexionistes de l’apprentissage. Ph.D. thesis, Université de
Paris VI. 18, 502, 515
LeCun, Y. (1989). Generalization and network design strategies. Technical Report
CRG-TR-89-4, University of Toronto. 330, 352
LeCun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon, I., Henderson, D.,
Howard, R. E., and Hubbard, W. (1989). Handwritten digit recognition: Applications
of neural network chips and automatic learning. IEEE Communications Magazine,
27(11), 41–46. 368
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K.-R. (1998a). Efficient backprop. In
Neural Networks, Tricks of the Trade, Lecture Notes in Computer Science LNCS 1524.
Springer Verlag. 310, 429
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998b). Gradient based learning
applied to document recognition. Proc. IEEE. 16, 18, 21, 27, 371, 458, 460
LeCun, Y., Kavukcuoglu, K., and Farabet, C. (2010). Convolutional networks and
applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE
International Symposium on, pages 253–256. IEEE. 371
L’Ecuyer, P. (1994). Efficiency improvement and variance reduction. In Proceedings of
the 1994 Winter Simulation Conference, pages 122––132. 690
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z. (2014). Deeply-supervised nets.
arXiv preprint arXiv:1409.5185 . 326
Lee, H., Battle, A., Raina, R., and Ng, A. (2007). Efficient sparse coding algorithms.
In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information
Processing Systems 19 (NIPS’06), pages 801–808. MIT Press. 637
Lee, H., Ekanadham, C., and Ng, A. (2008). Sparse deep belief net model for visual area
V2. In NIPS’07 . 255
Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. (2009). Convolutional deep belief
networks for scalable unsupervised learning of hierarchical representations. In L. Bottou
and M. Littman, editors, Proceedings of the Twenty-sixth International Conference on
Machine Learning (ICML’09). ACM, Montreal, Canada. 363, 683, 684
Lee, Y. J. and Grauman, K. (2011). Learning the easy things first: self-paced visual
category discovery. In CVPR’2011 . 328
Leibniz, G. W. (1676). Memoir using the chain rule. (Cited in TMME 7:2&3 p 321-332,
2010). 225
Lenat, D. B. and Guha, R. V. (1989). Building large knowledge-based systems; representa-
tion and inference in the Cyc project. Addison-Wesley Longman Publishing Co., Inc.
Leshno, M., Lin, V. Y., Pinkus, A., and Schocken, S. (1993). Multilayer feedforward
networks with a nonpolynomial activation function can approximate any function.
Neural Networks, 6, 861––867. 198, 199
Levenberg, K. (1944). A method for the solution of certain non-linear problems in least
squares. Quarterly Journal of Applied Mathematics, II(2), 164–168. 312
L’Hôpital, G. F. A. (1696). Analyse des infiniment petits, pour l’intelligence des lignes
courbes. Paris: L’Imprimerie Royale. 225
Li, Y., Swersky, K., and Zemel, R. S. (2015). Generative moment matching networks.
CoRR, abs/1502.02761. 703
Lin, T., Horne, B. G., Tino, P., and Giles, C. L. (1996). Learning long-term dependencies
is not as difficult with NARX recurrent neural networks. IEEE Transactions on Neural
Networks, 7(6), 1329–1338. 407
Lin, Y., Liu, Z., Sun, M., Liu, Y., and Zhu, X. (2015). Learning entity and relation
embeddings for knowledge graph completion. In Proc. AAAI’15 . 484
Linde, N. (1992). The machine that changed the world, episode 3. Documentary miniseries.
Lindsey, C. and Lindblad, T. (1994). Review of hardware neural networks: a user’s
perspective. In Proc. Third Workshop on Neural Networks: From Biology to High
Energy Physics, pages 195––202, Isola d’Elba, Italy. 451
Linnainmaa, S. (1976). Taylor expansion of the accumulated rounding error. BIT
Numerical Mathematics, 16(2), 146–160. 225
LISA (2008). Deep learning tutorials: Restricted Boltzmann machines. Technical report,
LISA Lab, Université de Montréal. 589
Long, P. M. and Servedio, R. A. (2010). Restricted Boltzmann machines are hard to
approximately evaluate or simulate. In Proceedings of the 27th International Conference
on Machine Learning (ICML’10). 658
Lotter, W., Kreiman, G., and Cox, D. (2015). Unsupervised learning of visual structure
using predictive generative networks. arXiv preprint arXiv:1511.06380 . 544, 545
Lovelace, A. (1842). Notes upon L. F. Menabrea’s Sketch of the Analytical Engine
invented by Charles Babbage”. 1
Lu, L., Zhang, X., Cho, K., and Renals, S. (2015). A study of the recurrent neural network
encoder-decoder for large vocabulary speech recognition. In Proc. Interspeech. 461
Lu, T., Pál, D., and Pál, M. (2010). Contextual multi-armed bandits. In International
Conference on Artificial Intelligence and Statistics, pages 485–492. 480
Luenberger, D. G. (1984). Linear and Nonlinear Programming. Addison Wesley. 316
Lukoševičius, M. and Jaeger, H. (2009). Reservoir computing approaches to recurrent
neural network training. Computer Science Review, 3(3), 127–149. 404
Luo, H., Shen, R., Niu, C., and Ullrich, C. (2011). Learning class-relevant features and
class-irrelevant features via a hybrid third-order RBM. In International Conference on
Artificial Intelligence and Statistics, pages 470–478. 686
Luo, H., Carrier, P. L., Courville, A., and Bengio, Y. (2013). Texture modeling with
convolutional spike-and-slab RBMs and deep extensions. In AISTATS’2013 . 102
Lyu, S. (2009). Interpretation and generalization of score matching. In Proceedings of the
Twenty-fifth Conference in Uncertainty in Artificial Intelligence (UAI’09). 618
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., and Svetnik, V. (2015). Deep neural nets
as a method for quantitative structure activity relationships. J. Chemical information
and modeling. 530
Maas, A. L., Hannun, A. Y., and Ng, A. Y. (2013). Rectifier nonlinearities improve neural
network acoustic models. In ICML Workshop on Deep Learning for Audio, Speech, and
Language Processing. 193
Maass, W. (1992). Bounds for the computational power and learning complexity of analog
neural nets (extended abstract). In Proc. of the 25th ACM Symp. Theory of Computing,
pages 335–344. 199
Maass, W., Schnitger, G., and Sontag, E. D. (1994). A comparison of the computational
power of sigmoid and Boolean threshold circuits. Theoretical Advances in Neural
Computation and Learning, pages 127–151. 199
Maass, W., Natschlaeger, T., and Markram, H. (2002). Real-time computing without
stable states: A new framework for neural computation based on perturbations. Neural
Computation, 14(11), 2531–2560. 404
MacKay, D. (2003). Information Theory, Inference and Learning Algorithms. Cambridge
University Press. 73
Maclaurin, D., Duvenaud, D., and Adams, R. P. (2015). Gradient-based hyperparameter
optimization through reversible learning. arXiv preprint arXiv:1502.03492 . 435
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., and Yuille, A. L. (2015). Deep captioning
with multimodal recurrent neural networks. In ICLR’2015 . arXiv:1410.1090. 102
Marcotte, P. and Savard, G. (1992). Novel approaches to the discrimination problem.
Zeitschrift für Operations Research (Theory), 36, 517–545. 276
Marlin, B. and de Freitas, N. (2011). Asymptotic efficiency of deterministic estimators for
discrete energy-based models: Ratio matching and pseudolikelihood. In UAI’2011 . 617,
Marlin, B., Swersky, K., Chen, B., and de Freitas, N. (2010). Inductive principles for
restricted Boltzmann machine learning. In Proceedings of The Thirteenth International
Conference on Artificial Intelligence and Statistics (AISTATS’10), volume 9, pages
509–516. 613, 618, 619
Marquardt, D. W. (1963). An algorithm for least-squares estimation of non-linear param-
eters. Journal of the Society of Industrial and Applied Mathematics,
(2), 431–441.
Marr, D. and Poggio, T. (1976). Cooperative computation of stereo disparity. Science,
194. 367
Martens, J. (2010). Deep learning via Hessian-free optimization. In L. Bottou and
M. Littman, editors, Proceedings of the Twenty-seventh International Conference on
Machine Learning (ICML-10), pages 735–742. ACM. 304
Martens, J. and Medabalimi, V. (2014). On the expressive efficiency of sum product
networks. arXiv:1411.7717 . 554
Martens, J. and Sutskever, I. (2011). Learning recurrent neural networks with Hessian-free
optimization. In Proc. ICML’2011 . ACM. 413
Mase, S. (1995). Consistency of the maximum pseudo-likelihood estimator of continuous
state space Gibbsian processes. The Annals of Applied Probability,
(3), pp. 603–612.
McClelland, J., Rumelhart, D., and Hinton, G. (1995). The appeal of parallel distributed
processing. In Computation & intelligence, pages 305–341. American Association for
Artificial Intelligence. 17
McCulloch, W. S. and Pitts, W. (1943). A logical calculus of ideas immanent in nervous
activity. Bulletin of Mathematical Biophysics, 5, 115–133. 14, 15
Mead, C. and Ismail, M. (2012). Analog VLSI implementation of neural systems, volume 80.
Springer Science & Business Media. 451
Melchior, J., Fischer, A., and Wiskott, L. (2013). How to center binary deep Boltzmann
machines. arXiv preprint arXiv:1311.1354 . 674
Memisevic, R. and Hinton, G. E. (2007). Unsupervised learning of image transformations.
In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR’07).
Memisevic, R. and Hinton, G. E. (2010). Learning to represent spatial transformations
with factored higher-order Boltzmann machines. Neural Computation,
(6), 1473–1492.
Mesnil, G., Dauphin, Y., Glorot, X., Rifai, S., Bengio, Y., Goodfellow, I., Lavoie, E.,
Muller, X., Desjardins, G., Warde-Farley, D., Vincent, P., Courville, A., and Bergstra,
J. (2011). Unsupervised and transfer learning challenge: a deep learning approach. In
JMLR W&CP: Proc. Unsupervised and Transfer Learning, volume 7. 201, 532, 538
Mesnil, G., Rifai, S., Dauphin, Y., Bengio, Y., and Vincent, P. (2012). Surfing on the
manifold. Learning Workshop, Snowbird. 711
Miikkulainen, R. and Dyer, M. G. (1991). Natural language processing with modular
PDP networks and distributed lexicon. Cognitive Science, 15, 343–399. 477
Mikolov, T. (2012). Statistical Language Models based on Neural Networks. Ph.D. thesis,
Brno University of Technology. 414
Mikolov, T., Deoras, A., Kombrink, S., Burget, L., and Cernocky, J. (2011a). Empirical
evaluation and combination of advanced language modeling techniques. In Proc. 12th an-
nual conference of the international speech communication association (INTERSPEECH
2011). 472
Mikolov, T., Deoras, A., Povey, D., Burget, L., and Cernocky, J. (2011b). Strategies for
training large scale neural network language models. In Proc. ASRU’2011. 328, 472
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word rep-
resentations in vector space. In International Conference on Learning Representations:
Workshops Track. 536
Mikolov, T., Le, Q. V., and Sutskever, I. (2013b). Exploiting similarities among languages
for machine translation. Technical report, arXiv:1309.4168. 539
Minka, T. (2005). Divergence measures and message passing. Microsoft Research Cambridge
UK Tech Rep MSRTR2005173 , 72(TR-2005-173). 625
Minsky, M. L. and Papert, S. A. (1969). Perceptrons. MIT Press, Cambridge. 15
Mirza, M. and Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint
arXiv:1411.1784 . 702
Mishkin, D. and Matas, J. (2015). All you need is a good init. arXiv preprint
arXiv:1511.06422 . 305
Misra, J. and Saha, I. (2010). Artificial neural networks in hardware: A survey of two
decades of progress. Neurocomputing, 74(1), 239–255. 451
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, New York. 99
Miyato, T., Maeda, S., Koyama, M., Nakae, K., and Ishii, S. (2015). Distributional
smoothing with virtual adversarial training. In ICLR. Preprint: arXiv:1507.00677. 269
Mnih, A. and Gregor, K. (2014). Neural variational inference and learning in belief
networks. In ICML’2014 . 691, 692, 693
Mnih, A. and Hinton, G. E. (2007). Three new graphical models for statistical language
modelling. In Z. Ghahramani, editor, Proceedings of the Twenty-fourth International
Conference on Machine Learning (ICML’07), pages 641–648. ACM. 465
Mnih, A. and Hinton, G. E. (2009). A scalable hierarchical distributed language model.
In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural
Information Processing Systems 21 (NIPS’08), pages 1081–1088. 467
Mnih, A. and Kavukcuoglu, K. (2013). Learning word embeddings efficiently with noise-
contrastive estimation. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and
K. Weinberger, editors, Advances in Neural Information Processing Systems 26 , pages
2265–2273. Curran Associates, Inc. 472, 622
Mnih, A. and Teh, Y. W. (2012). A fast and simple algorithm for training neural
probabilistic language models. In ICML’2012 , pages 1751–1758. 472
Mnih, V. and Hinton, G. (2010). Learning to detect roads in high-resolution aerial images.
In Proceedings of the 11th European Conference on Computer Vision (ECCV). 102
Mnih, V., Larochelle, H., and Hinton, G. (2011). Conditional restricted Boltzmann
machines for structure output prediction. In Proc. Conf. on Uncertainty in Artificial
Intelligence (UAI). 685
Mnih, V., Kavukcuoglo, K., Silver, D., Graves, A., Antonoglou, I., and Wierstra, D. (2013).
Playing Atari with deep reinforcement learning. Technical report, arXiv:1312.5602. 106
Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014). Recurrent models of visual
attention. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger,
editors, NIPS’2014 , pages 2204–2212. 691
Mnih, V., Kavukcuoglo, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves,
A., Riedmiller, M., Fidgeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A.,
Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015).
Human-level control through deep reinforcement learning. Nature, 518, 529–533. 25
Mobahi, H. and Fisher, III, J. W. (2015). A theoretical analysis of optimization by
Gaussian continuation. In AAAI’2015 . 327
Mobahi, H., Collobert, R., and Weston, J. (2009). Deep learning from temporal coherence
in video. In L. Bottou and M. Littman, editors, Proceedings of the 26th International
Conference on Machine Learning, pages 737–744, Montreal. Omnipress. 494
Mohamed, A., Dahl, G., and Hinton, G. (2009). Deep belief networks for phone recognition.
Mohamed, A., Sainath, T. N., Dahl, G., Ramabhadran, B., Hinton, G. E., and Picheny,
M. A. (2011). Deep belief networks using discriminative features for phone recognition. In
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference
on, pages 5060–5063. IEEE. 459
Mohamed, A., Dahl, G., and Hinton, G. (2012a). Acoustic modeling using deep belief
networks. IEEE Trans. on Audio, Speech and Language Processing,
(1), 14–22. 459
Mohamed, A., Hinton, G., and Penn, G. (2012b). Understanding how deep belief networks
perform acoustic modelling. In Acoustics, Speech and Signal Processing (ICASSP),
2012 IEEE International Conference on, pages 4273–4276. IEEE. 459
Moller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning.
Neural Networks, 6, 525–533. 316
Montavon, G. and Muller, K.-R. (2012). Deep Boltzmann machines and the centering
trick. In G. Montavon, G. Orr, and K.-R. Müller, editors, Neural Networks: Tricks of
the Trade, volume 7700 of Lecture Notes in Computer Science, pages 621–637. Preprint: 673
Montúfar, G. (2014). Universal approximation depth and errors of narrow belief networks
with discrete units. Neural Computation, 26. 553
Montúfar, G. and Ay, N. (2011). Refinements of universal approximation results for
deep belief networks and restricted Boltzmann machines. Neural Computation,
1306–1319. 553
Montufar, G. F., Pascanu, R., Cho, K., and Bengio, Y. (2014). On the number of linear
regions of deep neural networks. In NIPS’2014 . 19, 199, 200
Mor-Yosef, S., Samueloff, A., Modan, B., Navot, D., and Schenker, J. G. (1990). Ranking
the risk factors for cesarean: logistic regression analysis of a nationwide study. Obstet
Gynecol, 75(6), 944–7. 3
Morin, F. and Bengio, Y. (2005). Hierarchical probabilistic neural network language
model. In AISTATS’2005 . 467, 469
Mozer, M. C. (1992). The induction of multiscale temporal structure. In J. M. S. Hanson
and R. Lippmann, editors, Advances in Neural Information Processing Systems 4
(NIPS’91), pages 275–282, San Mateo, CA. Morgan Kaufmann. 407, 408
Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press,
Cambridge, MA, USA. 62, 98, 146
Murray, B. U. I. and Larochelle, H. (2014). A deep and tractable density estimator. In
ICML’2014 . 190, 710
Nair, V. and Hinton, G. (2010). Rectified linear units improve restricted Boltzmann
machines. In ICML’2010 . 16, 174, 197
Nair, V. and Hinton, G. E. (2009). 3d object recognition with deep belief nets. In Y. Bengio,
D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in
Neural Information Processing Systems 22 , pages 1339–1347. Curran Associates, Inc.
Narayanan, H. and Mitter, S. (2010). Sample complexity of testing the manifold hypothesis.
In NIPS’2010 . 164
Naumann, U. (2008). Optimal Jacobian accumulation is NP-complete. Mathematical
Programming, 112(2), 427–441. 222
Navigli, R. and Velardi, P. (2005). Structural semantic interconnections: a knowledge-
based approach to word sense disambiguation. IEEE Trans. Pattern Analysis and
Machine Intelligence, 27(7), 1075––1086. 485
Neal, R. and Hinton, G. (1999). A view of the EM algorithm that justifies incremental,
sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models. MIT
Press, Cambridge, MA. 634
Neal, R. M. (1990). Learning stochastic feedforward networks. Technical report. 692
Neal, R. M. (1993). Probabilistic inference using Markov chain Monte-Carlo methods.
Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto. 680
Neal, R. M. (1994). Sampling from multimodal distributions using tempered transitions.
Technical Report 9421, Dept. of Statistics, University of Toronto. 603
Neal, R. M. (1996). Bayesian Learning for Neural Networks. Lecture Notes in Statistics.
Springer. 265
Neal, R. M. (2001). Annealed importance sampling. Statistics and Computing,
125–139. 625, 627, 628
Neal, R. M. (2005). Estimating ratios of normalizing constants using linked importance
sampling. 629
Nesterov, Y. (1983). A method of solving a convex programming problem with convergence
rate O(1/k
). Soviet Mathematics Doklady, 27, 372–376. 300
Nesterov, Y. (2004). Introductory lectures on convex optimization : a basic course. Applied
optimization. Kluwer Academic Publ., Boston, Dordrecht, London. 300
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011). Reading
digits in natural images with unsupervised feature learning. Deep Learning and
Unsupervised Feature Learning Workshop, NIPS. 21
Ney, H. and Kneser, R. (1993). Improved clustering techniques for class-based statistical
language modelling. In European Conference on Speech Communication and Technology
(Eurospeech), pages 973–976, Berlin. 463
Ng, A. (2015). Advice for applying machine learning. 421
Niesler, T. R., Whittaker, E. W. D., and Woodland, P. C. (1998). Comparison of part-of-
speech and automatically derived category-based language models for speech recognition.
In International Conference on Acoustics, Speech and Signal Processing (ICASSP),
pages 177–180. 463
Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., and Barbano, P. E. (2005).
Toward automatic phenotyping of developing embryos from videos. Image Processing,
IEEE Transactions on, 14(9), 1360–1371. 360
Nocedal, J. and Wright, S. (2006). Numerical Optimization. Springer. 92, 96
Norouzi, M. and Fleet, D. J. (2011). Minimal loss hashing for compact binary codes. In
ICML’2011 . 525
Nowlan, S. J. (1990). Competing experts: An experimental investigation of associative
mixture models. Technical Report CRG-TR-90-5, University of Toronto. 450
Nowlan, S. J. and Hinton, G. E. (1992). Simplifying neural networks by soft weight-sharing.
Neural Computation, 4(4), 473–493. 139
Olshausen, B. and Field, D. J. (2005). How close are we to understanding V1? Neural
Computation, 17, 1665–1699. 16
Olshausen, B. A. and Field, D. J. (1996). Emergence of simple-cell receptive field properties
by learning a sparse code for natural images. Nature,
, 607–609. 147, 255, 370, 496
Olshausen, B. A., Anderson, C. H., and Van Essen, D. C. (1993). A neurobiological
model of visual attention and invariant pattern recognition based on dynamic routing
of information. J. Neurosci., 13(11), 4700–4719. 450
Opper, M. and Archambeau, C. (2009). The variational Gaussian approximation revisited.
Neural computation, 21(3), 786–792. 689
Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014). Learning and transferring mid-level
image representations using convolutional neural networks. In Computer Vision and
Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1717–1724. IEEE. 536
Osindero, S. and Hinton, G. E. (2008). Modeling image patches with a directed hierarchy
of Markov random fields. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors,
Advances in Neural Information Processing Systems 20 (NIPS’07), pages 1121–1128,
Cambridge, MA. MIT Press. 632
Ovid and Martin, C. (2004). Metamorphoses. W.W. Norton. 1
Paccanaro, A. and Hinton, G. E. (2000). Extracting distributed representations of concepts
and relations from positive and negative propositions. In International Joint Conference
on Neural Networks (IJCNN), Como, Italy. IEEE, New York. 484
Paine, T. L., Khorrami, P., Han, W., and Huang, T. S. (2014). An analysis of unsupervised
pre-training in light of recent advances. arXiv preprint arXiv:1412.6597 . 532
Palatucci, M., Pomerleau, D., Hinton, G. E., and Mitchell, T. M. (2009). Zero-shot
learning with semantic output codes. In Y. Bengio, D. Schuurmans, J. D. Lafferty,
C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing
Systems 22 , pages 1410–1418. Curran Associates, Inc. 539
Parker, D. B. (1985). Learning-logic. Technical Report TR-47, Center for Comp. Research
in Economics and Management Sci., MIT. 225
Pascanu, R., Mikolov, T., and Bengio, Y. (2013). On the difficulty of training recurrent
neural networks. In ICML’2013 . 289, 402, 403, 408, 414, 416
Pascanu, R., Gülçehre, Ç., Cho, K., and Bengio, Y. (2014a). How to construct deep
recurrent neural networks. In ICLR’2014 . 19, 265, 398, 399, 410, 460
Pascanu, R., Montufar, G., and Bengio, Y. (2014b). On the number of inference regions
of deep feed forward networks with piece-wise linear activations. In ICLR’2014 . 550
Pati, Y., Rezaiifar, R., and Krishnaprasad, P. (1993). Orthogonal matching pursuit:
Recursive function approximation with applications to wavelet decomposition. In Pro-
ceedings of the 27 th Annual Asilomar Conference on Signals, Systems, and Computers,
pages 40–44. 255
Pearl, J. (1985). Bayesian networks: A model of self-activated memory for evidential
reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society,
University of California, Irvine, pages 329–334. 563
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible
Inference. Morgan Kaufmann. 54
Perron, O. (1907). Zur theorie der matrices. Mathematische Annalen,
(2), 248–263. 597
Petersen, K. B. and Pedersen, M. S. (2006). The matrix cookbook. Version 20051003. 31
Peterson, G. B. (2004). A day of great illumination: B. F. Skinner’s discovery of shaping.
Journal of the Experimental Analysis of Behavior , 82(3), 317–328. 328
Pham, D.-T., Garat, P., and Jutten, C. (1992). Separation of a mixture of independent
sources through a maximum likelihood approach. In EUSIPCO, pages 771–774. 491