Full Professor / Professeur TitulairePolytechnique Montréal & MilaCanada CIFAR AI Chair,Distinguished Scientist ServiceNow Research

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Top row (real frames), bottom row (prediction)

Vikram Voleti, Alexia Jolicoeur-Martineau, Christopher Pal. "MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation" arXiv preprint arXiv:2205.09853 & NeurIPS 2022.

Project page: https://mask-cond-video-diffusion.github.io/

Roger Girgis, Florian Golemo, Felipe Codevilla, Jim Aldon D'Souza, Martin Weiss, Samira Ebrahimi Kahou, Felix Heide, and Christopher Pal. "Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction." arXiv preprint arXiv:2104.00563 (V1 Feb. 2021, V3 Feb. 2022) & ICLR 2022.

Project page:

Paul Barde, Tristan Karch, Derek Nowrouzezahrai, Clément Moulin-Frier, Christopher Pal, Pierre-Yves Oudeyer "Learning to Guide and to Be Guided in the Architect-Builder Problem." arXiv preprint arXiv:2112.07342 & ICLR 2022.

Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Christopher Pal, Michael Mozer. Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. NeurIPS 2021 (Datasets and Benchmarks Track)

Delay Correcting ϟ Actor Critic

Yann Bouteiller*, Simon Ramstedt*, Giovanni Beltrame, Chris Pal, Jonathan Binas. Reinforcement Learning with Random Delays. ICLR 2021 (*Equal contributions)

Yoshua Bengio*, Prateek Gupta*, Tegan Maharaj*, Nasim Rahaman*, Martin Weiss*, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams Predicting Infectiousness for Proactive Contact Tracing. ICLR 2021 (*Lead authors)

Torsten Scholak*, Raymond Li*, Dzmitry Bahdanau, Harm de Vries, Chris Pal. DuoRAT: Towards Simpler Text-to-SQL Models. NAACL 2021. (*Equal contributions)

Nicolas Gontier, Koustuv Sinha, Siva Reddy, Christopher Pal. Measuring Systematic Generalization in Neural Proof Generation with Transformers. NeurIPS 2020.

Julien Roy, Paul Barde, Félix G. Harvey, Derek Nowrouzezahrai, Christopher Pal. Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning. NeurIPS 2020.

Barde, P., Roy, J., Jeon, W., Pineau, J., Pal, C. and Nowrouzezahrai, D., 2020. Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization. NeurIPS 2020.

Rupprecht, Christian, Cyril Ibrahim, and Christopher J. Pal. "Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents." ICLR 2020.

A Casanova, PO Pinheiro, N Rostamzadeh, CJ Pal "Reinforced active learning for image segmentation". Project Github, ICLR 2020.

Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A. and Pal, C. (2020). A meta-transfer objective for learning to disentangle causal mechanisms. ICLR 2020.

Code on GitHub: https://github.com/authors-1901-10912/A-Meta-Transfer-Objective-For-Learning-To-Disentangle-Causal-Mechanisms

Félix G Harvey, Mike Yurick, Derek Nowrouzezahrai, Christopher Pal. "Robust motion in-betweening". SIGGRAPH 2020, ACM Transactions on Computer Graphics.

Beckham, Christopher, Sina Honari, Vikas Verma, Alex M. Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, and Chris Pal. "On Adversarial Mixup Resynthesis." NeurIPS 2019.

Project page and code on GitHub: https://github.com/christopher-beckham/amr

Ramstedt, Simon, and Chris Pal. "Real-Time Reinforcement Learning." NeurIPS 2019.

Project page and code on GitHub: https://github.com/rmst/rtrl

Lim, J.H., Pinheiro, P.O., Rostamzadeh, N., Pal, C., Ahn, S. Neural Multisensory Scene Inference. NeurIPS 2019.

Mehta, Bhairav, Manfred Diaz, Florian Golemo, Christopher J. Pal, and Liam Paull. "Active domain randomization." In the Conference on Robot Learning (CoRL). arXiv preprint arXiv:1904.04762 (2019).

Project page and code on GitHub:

Weiss, Martin, Simon Chamorro, Roger Girgis, Margaux Luck, Samira E. Kahou, Joseph P. Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, and Chris Pal. "Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments." In the Conference on Robot Learning (CoRL). arXiv preprint arXiv:1910.13249 (2019).

Project page: https://mweiss17.github.io/SEVN/

Piche, A., Thomas, V., Ibrahim, C., Bengio, Y., & Pal, C. (2019). Probabilistic Planning with Sequential Monte Carlo methods. In the proceedings of the International Conference on Learning Representations (ICLR).

Rostamzadeh, N., Hosseini, S., Boquet, T., Stokowiec, W., Zhang, Y., Jauvin, C. and Pal, C., 2018. Fashion-gen: The generative fashion dataset and challenge. arXiv preprint arXiv:1806.08317.

The FashionGen Challenge website: https://sites.google.com/view/cvcreative/fashion-gen

Li, Raymond, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. "Towards deep conversational recommendations." In Advances in neural information processing systems, pp. 9725-9735. 2018.

Project page: https://redialdata.github.io/website/

Subramanian, Sandeep, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, and Chris Pal. "Towards text generation with adversarially learned neural outlines." In Advances in Neural Information Processing Systems (NeurIPS), pp. 7551-7563. 2018.

See also: Subramanian, S., Rajeswar, S., Dutil, F., Pal, C. and Courville, A., 2017, August. Adversarial generation of natural language. In Proceedings of the 2nd Workshop on Representation Learning for NLP (pp. 241-251).

Subramanian, S., Trischler, A., Bengio, Y., and Pal, C.J. (2018) Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning. In the proceedings of the International Conference on Learning Representations (ICLR). (Appears online in OpenReview October 2017.)

Project page and code on GitHub: https://github.com/Maluuba/gensen

Trabelsi, C., Bilaniuk, O., Serdyuk, D., Subramanian, S., Santos, J.F., Mehri, S., Rostamzadeh, N., Bengio, Y. and Pal, C.J., (2018). Deep Complex Networks. In the proceedings of ICLR.

Code on GitHub: https://github.com/ChihebTrabelsi/deep_complex_networks

Serdyuk, D., Ke, N.R., Sordoni, A., Trischler, A., Pal, C. and Bengio, Y., (2018). Twin networks: Matching the future for sequence generation. In the proceedings of ICLR.

Ke, N.R., Goyal, A., Bilaniuk, O., Binas, J., Mozer, M.C., Pal, C. and Bengio, Y. (2018). Sparse attentive backtracking: Temporal credit assignment through reminding. In Proceedings of NeurIPS.

Code on GitHub: https://github.com/nke001/sparse_attentive_backtracking_release

Ke, R. N., Zołna, K., Sordoni, A., Lin, Z., Trischler, A., Bengio, Y., Pineau, J., Charlin, L., and Pal, C. (2018) Focused Hierarchical RNNs for Conditional Sequence Processing. In the proceedings of the International Conference on Machine Learning (ICML).

Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C. and Kautz, J., (2018). Improving Landmark Localization with Semi-Supervised Learning. In the proceedings of IEEE Computer Vision and Pattern Recognition (CVPR).

Vorontsov, E., Trabelsi, C., Kadoury, S. and Pal, C., (2017). On orthogonality and learning recurrent networks with long term dependencies. In the proceedings of ICML, pp. 3570-3578.

Maharaj, T., Ballas, N., Rohrbach, A., Courville, A. and Pal, C., (2017). A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. In the proceedings of CVPR.

Project page, code and dataset: https://github.com/teganmaharaj/MovieFIB

Krueger, D., Maharaj, T., Kramár, J., Pezeshki, M., Ballas, N., Ke, N.R., Goyal, A., Bengio, Y., Larochelle, H., Courville, A. and Pal, C., (2017). Zoneout: Regularizing RNNs by randomly preserving hidden activations. In the proceedings of ICLR.

GitHub code: https://github.com/teganmaharaj/zoneout

Witten, Ian H., Eibe Frank, Mark A. Hall, and Christopher J. Pal. Data Mining: Practical Machine Learning Tools and Techniques. 4th Edition. Morgan Kaufmann, 2016.

Honari, S., Yosinski, J., Vincent, P., & Pal, C. (2016). Recombinator networks: Learning coarse-to-fine feature aggregation. In the proceedings of CVPR, pp. 5743-5752.

Ballas, N., Yao, L., Pal, C., & Courville, A. (2015). Delving deeper into convolutional networks for learning video representations. In the proceedings of ICLR.

Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle and Aaron Courville, (2015) Describing Videos by Exploiting Temporal Structure. In the proceedings of the International Conference on Computer Vision (ICCV).

P Baudisch, C Pal, E Rudolph, D Steedly, R Szeliski, D Tan, M Uyttendaele. (2008) Real-time preview for panoramic images. US Patent 7,424,218.

Scharstein, Daniel, and Chris Pal. "Learning conditional random fields for stereo." In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8. IEEE, 2007.

Steedly, D., Pal, C. and Szeliski, R., 2005, October. Efficiently registering video into panoramic mosaics. In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 (Vol. 2, pp. 1300-1307). IEEE.

Pal, C., Szeliski, R., Uyttendaele, M. and Jojic, N., 2004, June. Probability models for high dynamic range imaging. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 2, pp. II-II). IEEE.