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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
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. External link
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. External link
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. (2021, May). Predicting infectiousness for proactive contact tracing [Paper]. 9th International Conference on Learning Representations (ICLR 2021), Vienne, Austria. Unavailable
Bengio, Y., Frejinger, E., Lodi, A., Patel, R., & Sankaranarayanan, S. (2020, September). A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for Stochastic Integer Programs [Paper]. 17th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research. (CPAIOR 2020), Vienna, Austria. External link
Bengio, Y., Deleu, T., Rahaman, N., Ke, N. R., Lachapelle, S., Bilaniuk, O., Goyal, A., & Pal, C. J. (2020, April). A meta-transfer objective for learning to disentagle causal mechanisms [Paper]. 8th International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia (27 pages). External link
Beckham, C., Honari, S., Verma, V., Lamb, A., Ghadiri, F., Hjelm, R. D., Bengio, Y., & Pal, C. J. (2019, December). On adversarial mixup resynthesis [Paper]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. External link
Bengio, Y., Lodi, A., & Prouvost, A. (2018). Machine learning for combinatorial optimization : a methodological tour d'horizon. (Technical Report n° DS4DM-2018-08). External link
Bengio, Y., & Lodi, A. (2017). Les données au service du savoir. Gestion, 42(1), 68-70. External link
Courbariaux, M., Bengio, Y., & David, J. P. (2015, December). BinaryConnect: Training deep neural networks with binary weights during propagations [Paper]. 28th Conference on Advances in Neural Information Processing Systems (NIPS 2015), Montréal, Québec. Unavailable
Courbariaux, M., Bengio, Y., & David, J. P. (2015, May). Training deep neural networks with low precision multiplications [Paper]. International Conference on Learning Representations (ICLR 2015), San Diego, Calif. (10 pages). External link
Carreau, J., & Bengio, Y. (2009). A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions. IEEE Transactions on Neural Networks, 20(7), 1087-1101. External link
Carreau, J., & Bengio, Y. (2009). A hybrid Pareto model for asymmetric fat-tailed data: the univariate case. Extremes, 12(1), 53-76. External link
Carreau, J., & Bengio, Y. (2007, March). A hybrid pareto model for conditional density estimation of asymmetric fat-tail data [Paper]. 11th International Conference on Artificial Intelligence and Statistics, San Pedro, Puerto Rico. Unavailable
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. External link
Gupta, P., Gasse, M., Khalil, E. B., Kumar, M. P., Lodi, A., & Bengio, Y. (2020, December). Hybrid models for learning to branch [Paper]. 34th Conference on neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (11 pages). External link
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. (2020, July). Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. [Paper]. 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria. External link
Gupta, P., Gasse, M., Khalil, E. B., Kumar, M. P., Lodi, A., & Bengio, Y. (2020, December). Supplement: Hybrid models for learning to branch [Paper]. 34th Conference on neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (8 pages). External link
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. External link
Goyal, A., Ke, N. R., Ganguli, S., & Bengio, Y. (2017, December). Variational walkback: Learning a transition operator as a stochastic recurrent net [Paper]. 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA. Unavailable
Goyal, A., Sordoni, A., Cote, M.-A., Ke, N. R., & Bengio, Y. (2017, December). Z-forcing: Training stochastic recurrent networks [Paper]. 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA. Unavailable
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. External link
Jegou, S., Drozdzal, M. A., Vazquez, D., Romero, A., & Bengio, Y. (2017, July). The one hundred layers Tiramisu: fully convolutional DenseNets for semantic segmentation [Paper]. IEEE Conference on Computer Vision and Pattern Recognition: Workshops (CVPRW 2017), Honolulu, HI, USA. External link
Kerg, G., Goyette, K., Touzel, M. P., Gidel, G., Vorontsov, E., Bengio, Y., & Lajoie, G. (2019, December). Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics [Paper]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, B.-C.. External link
Krueger, D., Maharaj, T., Kramar, J., Pezeshki, M., Ballas, N., Ke, N. R., Goyal, A., Bengio, Y., Courville, A., & Pal, C. J. (2017, April). Zoneout: Regularizing rNNs by randomly preserving hidden activations [Paper]. 5th International Conference on Learning Representations (ICLR 2017), Toulon, France (11 pages). External link
Ke, N. R., Zoma, K., Sordoni, A., Lin, Z., Trischler, A., Bengio, Y., Pineau, J., Charlin, L., & Pal, C. J. (2018, July). Focused hierarchical RNNs for conditional sequence processing [Paper]. 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden. External link
Ke, N. R., Goyal, A., Bilaniuk, O., Binas, J., Mozer, M. C., Pal, C. J., & Bengio, Y. (2018, December). Sparse attentive backtracking: Temporal credit assignment through reminding [Paper]. 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada (12 pages). External link
Kahou, S. E., Bouthillier, X., Lamblin, P., Gülçehre, Ç., 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. External link
Kahou, S. E., Pal, C. J., Bouthillier, X., Froumenty, P., Gülçehre, Ç., 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. (2013, December). Combining modality specific deep neural networks for emotion recognition in video [Paper]. 15th ACM International Conference on Multimodal Interaction (ICMI 2013), Sydney, NSW, Australia. External link
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. External link
Larsen, E., Lachapelle, S., Bengio, Y., Frejinger, E., Lacoste-Julien, S., & Lodi, A. (2019). Predicting tactical solutions to operational planning problems under imperfect information. (Technical Report n° DS4DM-2019-003). External link
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. External link
Madan, K., Ke, N. R., Goyal, A., Schölkopf, B., & Bengio, Y. (2021, April). Fast and slow learning of recurrent independent mechanisms [Paper]. 10th International Conference on Learning Representations (ICLR 2021) (18 pages). Unavailable
Piche, A., Thomas, V., Ibrahim, C., Bengio, Y., & Pal, C. J. (2019, May). Probabilistic planning with sequential Monte Carlo methods [Poster]. 7th International Conference on Learning Representations, New Orleans, Louisiana (8 pages). External link
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). Available
Sylvain, T., Luck, M., Cohen, J. P., Cardinal, H., Lodi, A., & Bengio, Y. (2021, March). Exploring the Wasserstein metric for survival analysis [Paper]. AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges and Applications (SPACA 2021), Palo Alto, CA, USA (13 pages). External link
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. External link
Sankar, C., Subramanian, S., Pal, C. J., Chandar, S., & Bengio, Y. (2019, July). Do neural dialog systems use the conversation history effectively? An empirical study [Paper]. 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy. External link
Subramanian, S., Trischler, A., Bengio, Y., & Pal, C. J. (2018, April). Learning general purpose distributed sentence representations via large scale multitask learning [Paper]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada. External link
Serdyuk, D., Ke, N. R., Sordoni, A., Trischler, A., Pal, C. J., & Bengio, Y. (2018, April). Twin Networks: Matching the future for sequence generation [Paper]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada (12 pages). External link
Serban, I. V., García-Durán, A., Gülçehre, Ç., Ahn, S., Anbil Parthipan, S. C., Courville, A., & Bengio, Y. (2016, August). Generating Factoid Questions with Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus [Paper]. 54th annual meeting of the Association for Computational Linguistics, Berlin, Germany. External link
Trabelsi, C., Bilaniuk, O., Zhang, Y., Serdyuk, D., Subramanian, S., Santos, J. F., Mehri, S., Rostamzadeh, N., Bengio, Y., & Pal, C. J. (2018, April). Deep complex networks [Paper]. 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada (19 pages). External link
Weiss, M., Rahaman, N., Locatello, F., Pal, C. J., Bengio, Y., Scholkopf, B., Ballas, N., & Li, L. E. (2022, November). Neural Attentive Circuits [Poster]. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA. External link
Yuan, X., Côté, M.-A., Fu, J., Lin, Z., Pal, C. J., Bengio, Y., & Trischler, A. (2019, November). Interactive language learning by question answering [Paper]. Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China. External link
Zarpellon, G., Jo, J., Lodi, A., & Bengio, Y. (2021, February). Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies [Paper]. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence. Published in Proceedings of the ... AAAI Conference on Artificial Intelligence, 35(5). External link
