Christopher Pal

Christopher Pal

Distinguished Scientist

Low Data Learning

Chris Pal is an associate professor in the Department of computer and software engineering at the École Polytechnique of Montreal and a Distinguished Scientist at ServiceNow Research. Prior to arriving in Montreal, he was a professor in the Department of Computer Science at the University of Rochester. He has been a research scientist with the University of Massachusetts and has also been affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research at Microsoft lead to three patents on image processing, computer vision and interactive multimedia. Chris earned his masters in Math and PhD from the University of Waterloo in Canada. His PhD research led to contributions applying probability models and optimization techniques to image, video and signal processing. Prior to his graduate studies Chris was with the multimedia research company Interval in Palo Alto, CA (Silicon Valley). As a result of his research at Interval he was awarded a patent on audio signal processing.

Publications

Attention-based Neural Cellular Automata. Conference on Neural Information Processing Systems (NeurIPS),  2022.

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DuoRAT: Towards Simpler Text-to-SQL Models. North American Chapter of the Association for Computational Linguistics (NAACL),  2021.

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Deep Complex Separators. Workshop at the Neural Information Processing Systems (NeurIPS),  2019.

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Neural Multisensory Scene Inference. Conference on Neural Information Processing Systems (NeurIPS),  2019.

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Real-Time Reinforcement Learning. Conference on Neural Information Processing Systems (NeurIPS),  2019.

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Structure Learning for Neural Module Networks. Annual Meeting of the Association for Computational Linguistics (ACL),  2019.

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Adversarial Mixup Resynthesizers. Workshop at the International Conference on Learning Representations (ICLR),  2019.

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Planning with Latent SImulated Trajectories. Workshop at the International Conference on Learning Representations (ICLR),  2019.

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Recurrent Transition Networks for Character Locomotion. Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia),  2018.

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