
BIBLIOGRAPHY
Judd, J. S. (1989). Neural Network Design and the Complexity of Learning. MIT press.
249
Jutten, C. and Herault, J. (1991). Blind separation of sources, part I: an adaptive algo-
rithm based on neuromimetic architecture. Signal Processing, 24, 1–10. 454
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., G¨ul¸cehre, c., Memisevic, R., Vin-
cent, 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., Ray-
mond, J.-P., Desjardins, G., Pascanu, R., Warde-Farley, D., Torabi, A., Sharma, A.,
Bengio, E., Cˆot´e, 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. 190
Kalchbrenner, N. and Blunsom, P. (2013). Recurrent continuous translation models. In
EMNLP’2013 . 407
Kamyshanska, H. and Memisevic, R. (2015). The potential energy of an autoencoder.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 468
Kanazawa, K., Koller, D., and Russell, S. (1995). Stochastic simulation algorithms for
dynamic probabilistic networks. In Proc. UAI’1995 , pages 346–351. 347
Karpathy, A. and Li, F.-F. (2015). Deep visual-semantic alignments for generating image
descriptions. In CVPR’2015 . arXiv:1412.2306. 95
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. 19
Karush, W. (1939). Minima of Functions of Several Variables with Inequalities as Side
Constraints. Master’s thesis, Dept.˜of Mathematics, Univ.˜of Chicago. 90
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. 348, 406
Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2008a). Fast inference in sparse coding
algorithms with applications to object recognition. CBLL-TR-2008-12-01, NYU. 447
Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2008b). 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. 462
Kavukcuoglu, K., Ranzato, M.-A., Fergus, R., and LeCun, Y. (2009). Learning invariant
features through topographic filter maps. In CVPR’2009. 462
Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., and LeCun, Y.
(2010a). Learning convolutional feature hierarchies for visual recognition. In Advances
in Neural Information Processing Systems 23 (NIPS’10), pages 1090–1098. 299
616