World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
23916
World Ranking
2535
National Ranking
93

Overview

Chris Pal is affiliated with Polytechnique Montréal in Canada and conducts research primarily in Computer Science. Their work spans various subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Electrical and Electronic Engineering, and Management Science and Operations Research.

Their research topics cover a range of areas within these fields, focusing on:

  • Machine Learning and Data Classification
  • Reinforcement Learning in Robotics
  • Generative Adversarial Networks and Image Synthesis
  • Speech Recognition and Synthesis
  • Natural Language Processing Techniques
  • Handwritten Text Recognition Techniques
  • Data Quality and Management

Chris Pal has contributed to several publication venues, with a significant number of papers appearing in arXiv (Cornell University). Other frequent venues include:

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • PolyPublie (École Polytechnique de Montréal)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Notable recent papers authored or co-authored by Chris Pal include:

  • "Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Role-Wise Data Augmentation for Knowledge Distillation" (2020), published on arXiv (Cornell University)
  • "Predicting Infectiousness for Proactive Contact Tracing" (2020), appearing in PolyPublie (École Polytechnique de Montréal)
  • "Workflow Discovery from Dialogues in the Low Data Regime" (2022), published on arXiv (Cornell University)
  • "ArK: Augmented Reality with Knowledge Interactive Emergent Ability" (2023), published on arXiv (Cornell University)

Collaboration is an element of their research, with frequent co-authors including Yoshua Bengio, Nasim Rahaman, Bernhard Schölkopf, Jie Fu, and David Vázquez. Each of these collaborators has co-authored multiple papers with Chris Pal, reflecting ongoing research partnerships.

Best Publications

  • Brain tumor segmentation with Deep Neural Networks

    Mohammad Havaei;Axel Davy;David Warde-Farley;Antoine Biard

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Describing Videos by Exploiting Temporal Structure

    Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • The Importance of Skip Connections in Biomedical Image Segmentation

    Michal Drozdzal;Eugene Vorontsov;Gabriel Chartrand;Samuel Kadoury

  • Learning Conditional Random Fields for Stereo

    D. Scharstein;C. Pal

  • Deep Learning: A Primer for Radiologists

    Gabriel Chartrand;Phillip M Cheng;Eugene Vorontsov;Michal Drozdzal

  • Activity recognition using the velocity histories of tracked keypoints

    Ross Messing;Chris Pal;Henry Kautz

  • Delving Deeper into Convolutional Networks for Learning Video Representations

    Nicolas Ballas;Li Yao;Chris Pal;Aaron Courville

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Recurrent Neural Networks for Emotion Recognition in Video

    Samira Ebrahimi Kahou;Vincent Michalski;Kishore Konda;Roland Memisevic

  • Real-time preview system and method for panoramic images

    Chris Pal;Matthew Uyttendaele;Eric Rudolph;Patrick Baudisch

  • Deep Complex Networks

    Chiheb Trabelsi;Olexa Bilaniuk;Ying Zhang;Dmitriy Serdyuk

  • Movie Description

    Anna Rohrbach;Atousa Torabi;Marcus Rohrbach;Niket Tandon

  • A Panoramic View of Yeast Noncoding RNA Processing

    Wen Tao Peng;Mark D. Robinson;Sanie Mnaimneh;Nevan J. Krogan

  • Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

    Sandeep Subramanian;Adam Trischler;Yoshua Bengio;Christopher J Pal

  • Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

    David Krueger;Tegan Maharaj;János Kramár;Mohammad Pezeshki

  • Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

    Michal Drozdzal;Michal Drozdzal;Gabriel Chartrand;Eugene Vorontsov;Mahsa Shakeri

  • Towards Deep Conversational Recommendations

    Raymond Li;Samira Ebrahimi Kahou;Hannes Schulz;Vincent Michalski

  • A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

    Yoshua Bengio;Tristan Deleu;Nasim Rahaman;Nan Rosemary Ke

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Aaron Courville
Aaron Courville University of Montreal
Pascal Vincent
Pascal Vincent Facebook (United States)
Samuel Kadoury
Samuel Kadoury Polytechnique Montréal
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Adam Trischler
Adam Trischler Microsoft (United States)
Hugo Larochelle
Hugo Larochelle Google (United States)
Nicolas Ballas
Nicolas Ballas Facebook (United States)
Ian H. Witten
Ian H. Witten University of Waikato
Richard Szeliski
Richard Szeliski University of Washington

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