Documents dont l'auteur est "Bengio, Yoshua"

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Nombre de documents: 44

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Anbil Parthipan, S. C., Sankar, C., Vorontsov, E., Kahou, S. E., & Bengio, Y. (2019). Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies. AAAI Conference on Artificial Intelligence, 33(1), 3280-3287. Lien externe

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Bengio, Y., Lodi, A., & Prouvost, A. (2021). Machine learning for combinatorial optimization: A methodological tour d'horizon. European Journal of Operational Research, 290(2), 405-421. Lien externe

Bengio, Y., Gupta, P., Maharaj, T., Rahaman, N., Weiss, M., Deleu, T., Muller, E., Qu, M., Schmidt, V., St-Charles, P.-L., Alsdurf, H., Bilanuik, O., Buckeridge, D., Caron, G. M., Carrier, P.-L., Ghosn, J., Ortiz-Gagne, S., Pal, C., Rish, I., ... Williams, A. (mai 2021). Predicting infectiousness for proactive contact tracing [Communication écrite]. 9th International Conference on Learning Representations (ICLR 2021), Vienne, Austria. Non disponible

Bengio, Y., Frejinger, E., Lodi, A., Patel, R., & Sankaranarayanan, S. (septembre 2020). A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for Stochastic Integer Programs [Communication écrite]. 17th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research. (CPAIOR 2020), Vienna, Austria. Lien externe

Bengio, Y., Deleu, T., Rahaman, N., Ke, N. R., Lachapelle, S., Bilaniuk, O., Goyal, A., & Pal, C. J. (avril 2020). A meta-transfer objective for learning to disentagle causal mechanisms [Communication écrite]. 8th International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia (27 pages). Lien externe

Beckham, C., Honari, S., Verma, V., Lamb, A., Ghadiri, F., Hjelm, R. D., Bengio, Y., & Pal, C. J. (décembre 2019). On adversarial mixup resynthesis [Communication écrite]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. Lien externe

Bengio, Y., Lodi, A., & Prouvost, A. (2018). Machine learning for combinatorial optimization : a methodological tour d'horizon. (Rapport technique n° DS4DM-2018-08). Non disponible

Bengio, Y., & Lodi, A. (2017). Les données au service du savoir. Gestion, 42(1), 68-70. Lien externe

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Courbariaux, M., Bengio, Y., & David, J. P. (décembre 2015). BinaryConnect: Training deep neural networks with binary weights during propagations [Communication écrite]. 28th Conference on Advances in Neural Information Processing Systems (NIPS 2015), Montréal, Québec. Non disponible

Courbariaux, M., Bengio, Y., & David, J. P. (mai 2015). Training deep neural networks with low precision multiplications [Communication écrite]. International Conference on Learning Representations (ICLR 2015), San Diego, Calif. (10 pages). Lien externe

Carreau, J., & Bengio, Y. (2009). A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions. IEEE Transactions on Neural Networks, 20(7), 1087-1101. Lien externe

Carreau, J., & Bengio, Y. (2009). A hybrid Pareto model for asymmetric fat-tailed data: the univariate case. Extremes, 12(1), 53-76. Lien externe

Carreau, J., & Bengio, Y. (mars 2007). A hybrid pareto model for conditional density estimation of asymmetric fat-tail data [Communication écrite]. 11th International Conference on Artificial Intelligence and Statistics, San Pedro, Puerto Rico. Non disponible

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Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., Romero, A., Bengio, Y., Pal, C. J., & Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical Image Analysis, 44, 1-13. Lien externe

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Gupta, P., Gasse, M., Khalil, E. B., Kumar, M. P., Lodi, A., & Bengio, Y. (décembre 2020). Hybrid models for learning to branch [Communication écrite]. 34th Conference on neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (11 pages). Lien externe

Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Thomas, K. M. J., Blackburn, S., Coley, C. W., Tang, J., Anbil Parthipan, S. C., & Bengio, Y. (juillet 2020). Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. [Communication écrite]. 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria. Lien externe

Gupta, P., Gasse, M., Khalil, E. B., Kumar, M. P., Lodi, A., & Bengio, Y. (décembre 2020). Supplement: Hybrid models for learning to branch [Communication écrite]. 34th Conference on neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (8 pages). Lien externe

Gulcehre, C., Anbil Parthipan, S. C., Cho, K., & Bengio, Y. (2018). Dynamic neural turing machine with continuous and discrete addressing schemes. Neural Computation, 30(4), 857-884. Lien externe

Goyal, A., Ke, N. R., Ganguli, S., & Bengio, Y. (décembre 2017). Variational walkback: Learning a transition operator as a stochastic recurrent net [Communication écrite]. 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA. Non disponible

Goyal, A., Sordoni, A., Cote, M.-A., Ke, N. R., & Bengio, Y. (décembre 2017). Z-forcing: Training stochastic recurrent networks [Communication écrite]. 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA. Non disponible

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Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C. J., Jodoin, P.-M., & Larochelle, H. (2017). Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis, 35, 18-31. Lien externe

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Jegou, S., Drozdzal, M. A., Vazquez, D., Romero, A., & Bengio, Y. (juillet 2017). The one hundred layers Tiramisu: fully convolutional DenseNets for semantic segmentation [Communication écrite]. IEEE Conference on Computer Vision and Pattern Recognition: Workshops (CVPRW 2017), Honolulu, HI, USA. Lien externe

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Kerg, G., Goyette, K., Touzel, M. P., Gidel, G., Vorontsov, E., Bengio, Y., & Lajoie, G. (décembre 2019). Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics [Communication écrite]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, B.-C.. Lien externe

Krueger, D., Maharaj, T., Kramar, J., Pezeshki, M., Ballas, N., Ke, N. R., Goyal, A., Bengio, Y., Courville, A., & Pal, C. J. (avril 2017). Zoneout: Regularizing rNNs by randomly preserving hidden activations [Communication écrite]. 5th International Conference on Learning Representations (ICLR 2017), Toulon, France (11 pages). Lien externe

Ke, N. R., Zoma, K., Sordoni, A., Lin, Z., Trischler, A., Bengio, Y., Pineau, J., Charlin, L., & Pal, C. J. (juillet 2018). Focused hierarchical RNNs for conditional sequence processing [Communication écrite]. 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden. Lien externe

Ke, N. R., Goyal, A., Bilaniuk, O., Binas, J., Mozer, M. C., Pal, C. J., & Bengio, Y. (décembre 2018). Sparse attentive backtracking: Temporal credit assignment through reminding [Communication écrite]. 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada (12 pages). Lien externe

Kahou, S. E., Bouthillier, X., Lamblin, P., Gulcehre, C., Michalski, V., Konda, K., Jean, S., Froumenty, P., Dauphin, Y., Boulanger-Lewandowski, N., Ferrari, R. C., Mirza, M., Warde-Farley, D., Courville, A., Vincent, P., Memisevic, R., Pal, C. J., & Bengio, Y. (2016). EmoNets: Multimodal deep learning approaches for emotion recognition in video. Journal on Multimodal User Interfaces, 10(2), 99-111. Lien externe

Kahou, S. E., Pal, C. J., Bouthillier, X., Froumenty, P., Gulcehre, 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., ... Wu, Z. (décembre 2013). Combining modality specific deep neural networks for emotion recognition in video [Communication écrite]. 15th ACM International Conference on Multimodal Interaction (ICMI 2013), Sydney, NSW, Australia. Lien externe

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Larsen, E., Lachapelle, S., Bengio, Y., Frejinger, E., Lacoste-Julien, S., & Lodi, A. (2021). Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information. INFORMS Journal on Computing, 34(1), 227-242. Lien externe

Larsen, E., Lachapelle, S., Bengio, Y., Frejinger, E., Lacoste-Julien, S., & Lodi, A. (2019). Predicting tactical solutions to operational planning problems under imperfect information. (Rapport technique n° DS4DM-2019-003). Lien externe

L'Heureux, P. J., Carreau, J., Bengio, Y., Delalleau, O., & Yue, S. Y. (2004). Locally Linear Embedding for dimensionality reduction in QSAR. Journal of Computer-Aided Molecular Design, 18(7), 475-482. Lien externe

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Madan, K., Ke, N. R., Goyal, A., Schölkopf, B., & Bengio, Y. (avril 2021). Fast and slow learning of recurrent independent mechanisms [Communication écrite]. 10th International Conference on Learning Representations (ICLR 2021) (18 pages). Non disponible

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Piche, A., Thomas, V., Ibrahim, C., Bengio, Y., & Pal, C. J. (mai 2019). Probabilistic planning with sequential Monte Carlo methods [Affiche]. 7th International Conference on Learning Representations, New Orleans, Louisiana (8 pages). Lien externe

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Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Körding, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., ... Bengio, Y. (2023). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 42 (96 pages). Disponible

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Sylvain, T., Luck, M., Cohen, J. P., Cardinal, H., Lodi, A., & Bengio, Y. (mars 2021). Exploring the Wasserstein metric for survival analysis [Communication écrite]. AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges and Applications (SPACA 2021), Palo Alto, CA, USA (13 pages). Lien externe

Serban, I. V., Sankar, C., Pieper, M., Pineau, J., & Bengio, Y. (2020). The Bottleneck Simulator: A Model-Based Deep Reinforcement Learning Approach. Journal of Artificial Intelligence Research, 69, 571-612. Lien externe

Sankar, C., Subramanian, S., Pal, C. J., Chandar, S., & Bengio, Y. (juillet 2019). Do neural dialog systems use the conversation history effectively? An empirical study [Communication écrite]. 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy. Lien externe

Subramanian, S., Trischler, A., Bengio, Y., & Pal, C. J. (avril 2018). Learning general purpose distributed sentence representations via large scale multitask learning [Communication écrite]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada. Lien externe

Serdyuk, D., Ke, N. R., Sordoni, A., Trischler, A., Pal, C. J., & Bengio, Y. (avril 2018). Twin Networks: Matching the future for sequence generation [Communication écrite]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada (12 pages). Lien externe

Serban, I. V., García-Durán, A., Gulcehre, C., Ahn, S., Anbil Parthipan, S. C., Courville, A., & Bengio, Y. (août 2016). Generating Factoid Questions with Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus [Communication écrite]. 54th annual meeting of the Association for Computational Linguistics, Berlin, Germany. Lien externe

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Trabelsi, C., Bilaniuk, O., Zhang, Y., Serdyuk, D., Subramanian, S., Santos, J. F., Mehri, S., Rostamzadeh, N., Bengio, Y., & Pal, C. J. (avril 2018). Deep complex networks [Communication écrite]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada (19 pages). Lien externe

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Weiss, M., Rahaman, N., Locatello, F., Pal, C. J., Bengio, Y., Scholkopf, B., Ballas, N., & Li, L. E. (novembre 2022). Neural Attentive Circuits [Affiche]. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA. Lien externe

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Yuan, X., Cote, M.-A., Fu, J., Lin, Z., Pal, C. J., Bengio, Y., & Trischler, A. (novembre 2019). Interactive language learning by question answering [Communication écrite]. Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China. Lien externe

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Zarpellon, G., Jo, J., Lodi, A., & Bengio, Y. (février 2021). Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies [Communication écrite]. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence. Publié dans Proceedings of the ... AAAI Conference on Artificial Intelligence, 35(5). Lien externe

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