Oral Presentations

  1. Cardiac MRI Segmentation with Strong Anatomical Guarantees, Nathan Painchaud (Université de Sherbrooke); Youssef Skandrani (Universite de Bourgogne Franche-Comté); Thierry Judge (Université de Sherbrooke); Olivier Bernard (Creatis); Alain Lalande (Universite de Bourgogne Franche-Comté); Pierre-Marc Jodoin (Universite de Sherbrooke)
  2. Temporal Knowledge Graph Completion, Rishab Goel (Borealis AI); Seyed Mehran Kazemi (Borealis AI); Marcus Brubaker (Borealis AI); Pascal Poupart (Borealis AI)
  3. Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning, Mahmoud Assran (McGill University / Facebook AI Research); Joshua Romoff (McGill University); Nicolas Ballas (Facebook FAIR); Joelle Pineau (Facebook); Mike Rabbat (Facebook FAIR)
  4. Tackling Societal Problems of Climate Change with Machine Learning, Tegan Maharaj (MILA, Polytechnic Montreal); Sasha Luccioni (Mila); Kris Sankaran (Montreal Institute for Learning Algorithms)
  5. Gradient-Based Neural DAG Learning, Sebastien Lachapelle (Mila, Université de Montréal); Philippe Brouillard (Mila); Tristan Deleu (Mila, Université de Montréal); Simon Lacoste-Julien (Mila, Université de Montréal)
  6. Kotlin∇: A Shape Safe eDSL for Differentiable Functional Programming, Breandan M Considine (Mila); Liam Paull (Université de Montréal); Michalis Famelis (Université de Montréal)
  7. Deep learning techniques applied to thermal inspection of the underground distribution cables, Arnaud Zinflou (Hydro-Québec); Michel Trepanier (Hydro-Québec); Olfa Ben Sik Ali (Hydro-Québec); Luc Cauchon (Hydro-Québec); François Miralles (Hydro-Québec); Marc-Andre Magnan (Hydro-Québec); Jonathan Racine (Hydro-Québec)
  8. Reducing Noise in GAN Training with Variance Reduced Extragradient, Tatjana Chavdarova (Mila); Gauthier Gidel (Mila, Université de Montréal, Element AI); Francois Fleuret (Idiap Research Institute); Simon Lacoste-Julien (Mila, Université de Montréal)
  9. Recurrent Language Modeling with Multiplicative RNNs, Diego Maupomé (UQÀM); Marie-Jean Meurs (UQAM)
  10. Verifying Individual Fairness in Neural Networks, Golnoosh Farnadi (Polytechnique Montreal); Behrouz Babaki (Polytechnique Montreal); Michel Gendreau (Polytechnique Montreal)

Poster Presentations

  1. Reconstruction of Apical Two-Chamber View in Echocardiogram with Generative Encoder-Decoder Modeling, Akinori Higaki (Lady Davis Institute for Medical Research); Katsuji Inoue (Ehime University Graduate School of Medicine); Masaki Kinoshita (Ehime University Graduate School of Medicine); Shuntaro Ikeda (Ehime University Graduate School of Medicine); Osamu Yamaguchi (Ehime University Graduate School of Medicine)
  2. Interpretation of the Coronary Angioscopy with Deep Convolutional Neural Networks, Akinori Higaki (Lady Davis Institute for Medical Research); Toru Miyoshi (Osaka University Hospital); Hideo Kawakami (Ehime Prefectural Imabari Hospital); Osamu Yamaguchi (Ehime University Graduate School of Medicine)
  3. Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks, Azadeh Mozafari (Laval University); Hugo S Gomes (Laval University); Wilson Leao (Petrobras); Christian Gagné (Université Laval)
  4. A Principled Approach for Learning Task Similarity in Multitask Learning, Changjian Shui (Université Laval); Mahdieh Abbasi (Université Laval); Louis-Émile Robitaille (Université Laval); Boyu Wang (University of Pennsylvania); Christian Gagné (Université Laval)
  5. Adaptive Deep Kernel Learning, Prudencio Tossou (InVivo and Université Laval); Basile Dura (InVivo AI and MILA); Mario Marchand (Université Laval); Francois Laviolette (Université Laval); Alexandre Lacoste (Element AI)
  6. A Python Framework for Neat Machine Learning Pipelines, Guillaume Chevalier (Neuraxio)
  7. On the Sensitivity of RNN decoders, Nicolas Gontier (Mila, Polytechnique Montreal, Element AI); Chris J Pal (MILA, Polytechnique Montréal, Element AI)
  8. Fast Spatially-Varying Indoor Lighting Estimation, Mathieu Garon (Université Laval); Jean-Francois Lalonde (Université Laval)
  9. All-Weather Deep Outdoor Lighting Estimation, Jinsong Zhang (Université Laval); Kalyan Sunkavalli (Adobe Research); Yannick Hold-Geoffroy (Adobe Research); Sunil Hadap (Adobe); Jonathan Eisenman (Adobe Systems); Jean-Francois Lalonde (Université Laval)
  10. Structured Pruning of Neural Networks with Budget-Aware Regularization, Carl Lemaire (Universite de Sherbrooke); Pierre-Marc Jodoin (Universite de Sherbrooke)
  11. The Difficulty of Training Sparse Neural Networks, Utku Evci (Google AI); Fabian Pedregosa (Google); Erich Elsen (Google); Aidan Gomez ()
  12. Unsupervised State Representation Learning in Atari, Ankesh Anand (Mila); Evan Racah (Mila); Sherjil Ozair (Mila); Yoshua Bengio (Mila); Marc-Alexandre Côté (Microsoft Research); R Devon Hjelm (Microsoft Research)
  13. Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring, Lucas May Petry (Federal University of Santa Catarina); Amilcar Soares (Dalhousie University); Vania Bogorny (Federal University of Santa Catarina); Stan Matwin (Dalhouise University)
  14. On Direct Distribution Matching for Adapting Segmentation Networks, Georg Pichler (CentraleSupélec); Jose Dolz (ETS); Ismail Ben Ayed (ETS Montreal); Pablo Piantanida (CentraleSupélec – Mila)
  15. Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation, Md. Akmal Haidar (Huawei Technologies); Mehdi Rezagholizadeh (Huawei Technologies); Alan Do-Omri (Huawei Technologies); Ahmad Rashid (Huawei Technologies)
  16. Bilingual-GAN: A Step Towards Parallel Text Generation, Ahmad Rashid (Huawei Technologies); Alan Do-Omri (Huawei Technologies); Md. Akmal Haidar (Huawei Technologies); Qun Liu (Huawei Noah’s Ark Lab); Mehdi Rezagholizadeh (Huawei Technologies)
  17. Using a Hybrid Generative Model Approach to Visualize Climate Change, Sasha Luccioni (Mila); Gautier Cosne (Mila); Sahil Bansal (Mila); Yoshua Bengio (Mila); Victor Schmidt (Mila)
  18. Generative Adversarial Learning for Unsupervised Person Re-Identification, Yacine Khraimeche (LITIV lab., Polytechnique Montreal); Guillaume-Alexandre Bilodeau (Polytechnique Montréal)
  19. Enhancing PIO Extraction from Abstract and Full Text of Medical Articles using Contextualised Embeddings, Hichem Mezaoui (IMRSV DATA LABS)
  20. Feature Fusion Architecture for Detection of Road Users in Video, Hughes Perreault (Polytechnique Montréal); Guillaume-Alexandre Bilodeau (Polytechnique Montréal); Nicolas Saunier (Polytechnique Montreal); Pierre Gravel (Genetec); Maguelonne Heritier (Genetec)
  21. Preference-Based Density Estimation, Vincent Dumoulin (Google); Pablo Samuel Castro (Google); Laurent Dinh (Google Research); Jesse Engel (Google); Hugo Larochelle (Google)
  22. A Modern Take on the Bias-Variance Tradeoff in Neural Networks, Brady Neal (Mila, Université de Montréal); Sarthak Mittal (MILA); Aristide Baratin (Mila, Université de Montréal); Vinayak Tantia (MILA); Matthew Scicluna (MILA); Simon Lacoste-Julien (Mila, Université de Montréal); Ioannis Mitliagkas (Mila & University of Montreal)
  23. GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects, Edward J Smith (McGill); Scott Fujimoto (McGill University); Adriana Romero (FAIR); David Meger (McGill University)
  24. Spectral Metric for Dataset Complexity Assessment, Frederic Branchaud-Charron (Universite de Sherbrooke); Andrew Achkar (Miovision Technologies Inc., Canada); Pierre-Marc Jodoin (Universite de Sherbrooke)
  25. Centroid Networks for Few-shot Clustering and Unsupervised Few-Shot Classification, Gabriel Huang (Mila & U.Montréal); Hugo Larochelle (Google); Simon Lacoste-Julien (Mila, Université de Montréal)
  26. Neural machine translation algorithms for predicting solutions to stochastic programming problems, Emma Frejinger (University of Montreal); Eric Larsen (Université de Montréal)
  27. Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification, Léonard Boussioux (Ecole Centrale Paris, Mila, MIT); Charles Guille-Escuret (U. Montreal, Mila); Tomas Giro-Larraz (Ecole Centrale Paris, EPFL); Mehdi Cherti (Mines Paristech); Balaszs Kegl (ChaLearn)
  28. Deep Parametric Indoor Lighting Estimation, Marc-André Gardner (Université Laval); Christian Gagné (Université Laval); Jean-Francois Lalonde (Université Laval)
  29. Fourier Bases for Reinforcement Learning on Combinatorial Puzzles, Horace Pan (UChicago); Risi Kondor (U. Chicago)
  30. Off-Policy Deep Reinforcement Learning without Exploration, Scott Fujimoto (McGill University); David Meger (McGill University); Doina Precup (McGill University)
  31. Learning what to learn using replay memory, Raymond Chua (McGill University); Margarida Carvalho (Université de Montréal); Rui Ponte Costa (University of Bristol); Doina Precup (McGill University)
  32. A Vector Representation of Time, Seyed Mehran Kazemi (Borealis AI); Rishab Goel (Borealis AI); Sepehr Eghbali (Borealis AI); Janahan Ramanan (Borealis AI); Jaspreet Sahota (Borealis AI); Sanjay Thakur (Borealis AI); Stella Wu (Borealis AI); Cathal Smyth (Royal Bank of Canada); Pascal Poupart (Borealis AI); Marcus Brubaker (Borealis AI)
  33. Towards Safer X-rays, Paria Kargar Samani (McMaster University); Shahram Shiranin (McMaster University)
  34. TT-Transformer: Tensorizing the Transformer, Charles C Onu (McGill University)
  35. SALSA-TEXT : Self Attentive Latent Space Based Adversarial Text Generation, Jules Gagnon-Marchand (Huawei Technologies); Hamed Sadeghi (Huawei Technologies ); Md. Akmal Haidar (Huawei Technologies); Mehdi Rezagholizadeh (Huawei Technologies)
  36. Cycle-consistent 3D rotations via autoencoders, Christopher Beckham (Ecole Polytechnique de Montreal); Derek Nowrouzezahrai (McGill University); Christopher Pal (École Polytechnique de Montréal )
  37. Recommender system pre-selection strategies with a reduced feature set, Adam Woznica (Expedia Group); Jan Krasnodebski (Expedia Group)
  38. A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games, Waïss Azizian (Mila, University of Montreal, Ecole Normale Supérieure de Paris); Ioannis Mitliagkas (Mila & University of Montreal); Simon Lacoste-Julien (Mila, Université de Montréal); Gauthier Gidel (Mila, Université de Montréal, Element AI)
  39. Lower Bounds and Conditioning of Differentiable Games, Adam Ibrahim (Mila, Université de Montréal); Waïss Azizian (Mila, University of Montreal, Ecole Normale Supérieure de Paris); Gauthier Gidel (Mila, Université de Montréal, Element AI); Ioannis Mitliagkas (Mila & University of Montreal)
  40. Option Discovery by Aiming to Predict, Veronica Chelu (McGill University); Doina Precup (McGill University)
  41. FoCL: Feature-oriented Continual Learning, Qicheng Lao (Concordia University); Mehrzad Mortazavi (Concordia University); Ahmad Pesaranghader (Dalhousie University); Marzieh Tahaei (McGill Unviersity); Francis Dutil (Imagia); Thomas Fevens (Concordia University); Mohammad Havaei (Imagia)
  42. ExTra: Transfer guided Exploration, Rishabh Madan (Indian Institute of Technology Kharagpur); Anirban Santara (Indian Institute of Technology Kharagpur); Balaraman Ravindran (Indian Institute of Technology, Madras); Pabitra Mitra (IIT Kharagpur)
  43. In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study, Brady Neal (Mila, Université de Montréal); Ioannis Mitliagkas (Mila & University of Montreal)
  44. DENN: Diverse Extrapolation in Neural Networks, Maxime Wabartha (McGill University); Audrey Durand (McGill University); Vincent François-Lavet (McGill University); Joelle Pineau (McGill / Facebook)
  45. Online Continual Learning with Maximally Interfered Retrieval, Rahaf Aljundi*, Lucas Caccia*, Eugene Belilovsky*, Massimo Caccia* (Mila), Min Lin, Laurent Charlin, Tinne Tuytelaars
  46. A Closer Look at the Optimization Landscapes of Generative Adversarial Networks, Hugo Berard (Mila, Facebook); Gauthier Gidel (Mila, Université de Montréal, Element AI); Amjad Almahairi (Element AI); Pascal Vincent (Facebook FAIR & MILA Université de Montréal); Simon Lacoste-Julien (Mila, Université de Montréal)
  47. Towards Interpretable Molecular Graph Representation Learning, Julien Horwood (InVivo AI); Emmanuel Noutahi (InVivo AI); Dominique Beani (InVivo AI); Prudencio Tossou (InVivo and Université Laval)
  48. Towards fingerprint-based drug generation, Basile Dura (InVivo AI and MILA); Prudencio Tossou (InVivo and Université Laval)
  49. A Data-Driven Approach for the Accurate Characterization of Drug-Drug Interactions, Mazid A OSSENI (Laval University); Rogia Kpanou (Laval University/ Invivo AI); Prudencio Tossou (Laval University/ InvivoAI)
  50. The Geometry of Sign Gradient Descent, Lukas Balles (University of Tuebingen); Fabian Pedregosa (Google); Nicolas Le Roux (Google)
  51. Towards Using Deep Learning for Plan and Intent Recognition, Mariane Maynard (Université de Sherbrooke); Thibault Duhamel (Université de Sherbrooke); Froduald Kabanza (Université de Sherbrooke)
  52. CT-SGAN: Computed Tomography with 3D Synthetic Medical Images using Recurrent Generative Adversarial Networks, Ahmad Pesaranghader (Dalhousie University); Lisa Di Jorio (Imagia Cybernetics Inc); Francis Dutil (Imagia); Tess Berthier (Imagia); Qicheng Lao (Concordia University); Mohammad Havaei (Imagia)
  53. Efficient Goal-Directed Molecular Synthesis Using Reinforcement Learning, Julien Horwood (InVivo AI); Emmanuel Noutahi (InVivo AI); Prudencio Tossou (InVivo and Université Laval)
  54. An hetero-modal framework for medical image synthesis applied to MRI, Daniel Gourdeau (Université Laval); Simon Duchesne (Université Laval); Louis Archambault (Université Laval)
  55. Information matrices and generalization, Valentin Thomas (Mila, Université de Montréal); Fabian Pedregosa (Google); Bart van Merriënboer (Google); Pierre-Antoine Manzagol (Google); Yoshua Bengio (Mila); Nicolas Le Roux (Google)
  56. GEAR: Geometry-Aware Rényi Information, Jose D Gallego Posada (Mila, Université de Montréal); Ankit Vani (Mila, Université de Montréal); Max Schwarzer (Mila, Université de Montréal); Simon Lacoste-Julien (Mila, Université de Montréal)
  57. Do Causal Models Adapt Faster to Interventions?, Rémi Le Priol (Mila & University of Montreal); Yoshua Bengio (Mila); Simon Lacoste-Julien (Mila, Université de Montréal)
  58. A grid-based deep learning approach for human activity recognition in realistic environments, Soufiane Lamghari (Polytechnique Montreal); Guillaume-Alexandre Bilodeau (Polytechnique Montréal); Nicolas Saunier (Polytechnique Montreal)
  59. Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives, Shagun Sodhani (MILA); Anirudh Goyal (University of Montreal); Jonathan Binas (Mila, Montreal); Xue Bin Peng (UC Berkeley); Sergey Levine (UC Berkeley); Yoshua Bengio (Mila)
  60. Mimicking the Physics of Radiation Treatment Planning with Neural Networks, Cem Subakan (MILA); Marc Andre Renaud (Polytechnique Montreal); Laurent Charlin (University of Toronto); Louis-Martin Rousseau (Polytechnique Montreal)
  61. Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies, Ethan Tseng (Princeton University); Felix Yu (Princeton University); Yuting Yang (Princeton University); Fahim Mannan (Algolux); Karl St-Arnaud (Algolux); Derek Nowrouzezahrai (McGill University); Jean-Francois Lalonde (Université Laval); Heide Felix (Stanford University)
  62. Learning Lexical Subspaces in the Distributional Vector Space, Kushal Aora (MILA); Aishik Chakraborty (McGill University/MILA); Jackie Chi Kit Cheung (McGill University / Mila)
  63. Enhancing clinical outcome prediction with notes, Mohammad Hashir (Mila, Université de Montréal)
  64. Fast Adaptation and Slow Meta-Learning of Neural Causal Models, Nan Rosemary Ke (Mila, University of Montreal); Olexa Bilaniuk (MILA, Polytechnique Montreal); Anirudh Goyal (University of Montreal); Chris J Pal (MILA, Polytechnique Montréal, Element AI); Yoshua Bengio (Mila)
  65. Recurrent Adversarial Training for Smart Meters Privacy, Mohammadhadi Shateri (Mcgill University); Francisco Messina (McGill University); Pablo Piantanida (CentraleSupélec – Mila); Labeau Fabrice (McGill University)
  66. Attentive Eligibility: Re-examining the Credit Assignment Problem in the Function Approximation Setting, Basile Dura; Julien Horwood (Mila); Clément Jumel (Mila)
  67. Energy-efficient binarized neural networks with unreliable memory cells, Henwood Sebastien (Polytechnique Montreal); Leduc-Primeau François (Polytechnique Montreal)
  68. Analysis of Recurrent Neural Network Models for Visual Working Memory : Towards Brain Modelling, Pravish Sainath (Universite_ de Montréal); Guillaume Lajoie (Université de Montréal, Mila)
  69. Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment, Adrien Ali Taiga (MILA, Université de Montréal); Marlos C. Machado (Google Brain); Marc G. Bellemare (Google Brain); Aaron Courville (MILA, Université de Montréal); Liam Fedus (Google)
  70. Analysis of Features for Long Term Popularity Prediction of Youtube Videos, Sri Charan Reddy Ragireddy (IIT Kharagpur)
  71. Deep Demosaicing, Vahid Partovi Nia (Huawei Noah’s Ark Lab); Ramchalam Ramakrishnan (Huawei Noah’s Ark Lab); Shangling Jui (Huawei Kirin Solution)
  72. Foothill Regularizer as a Binary Quantizer, Vahid Partovi Nia (Huawei Noah’s Ark Lab); Mouloud Belbahri (Huawei Technologies); Matthieu Courbariaux (universite de montreal); Eyyüb Sari (Huawei Technologies); Xinlin Li (Huawei Noah’s Ark Lab)
  73. Network sampling and applications to big data and machine learning, Antoine Rebecq (Shopify / Universite Paris Nanterre)
  74. AI-empowered Decision Support System for Early Diagnosis of Rheumatoid Arthritis in Primary Care using scare dataset, Samira Rahimi (McGill University)
  75. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, Boris Oreshkin (Element AI); Dmitri Carpov (Element AI); Nicolas Chapados (Element AI & Imagia); Yoshua Bengio (Mila)
  76. DeepRepair: A framework for error detection and correction, Meghana Moorthy Bhat (The Ohio State University); Yogesh Chellappa (University of Wisconsin Madison); Manjunath Shettar (University of Wisconsin Madison)
  77. An Optical Braille Recognition System for Enhancing Braille Literacy and Communication between The Blind and Non-blind, Wan-Chun Su (McGill University); Ting-Yi Su (McGill University); Helen Gezahegn (University of Alberta); Marin Ito (McGill Unversity)
  78. Towards Reducing Bias in Gender Classification, Komal Teru (Mila, McGill University)
  79. Striving for Simplicity in Off-policy Deep Reinforcement Learning, Rishabh Agarwal (Google Research, Brain Team); Dale Schuurmans (Google / University of Alberta); Mohammad Norouzi (Google Brain)
  80. Affective Computing to detect Depression, Manan Dey (SAP LABS); Shanya Sharma (SAP LABS)
  81. Patents, Innovation and Growth in Canadian Pharmaceuticals, Marlène KOFFI (University of Montreal); Vasia Panousi (University of Montreal)
  82. Approximate dynamic programming and reinforcement learning for partially observed systems, Jayakumar Subramanian (McGill University); Aditya Mahajan (McGill University)
  83. Quadcopter Navigation by Scene Recognition with Deep Reinforcement Learning, Dong Wang (Polytechnique de Montreal); Giovanni Beltrame (Polytechnique de Montreal)
  84. Data-Efficient Decentralized Place Recognition with 3D Constellations of Objects, Benjamin Ramtoula (Polytechnique Montréal / EPFL); Ricardo de Azambuja (Polytechnique Montreal); Giovanni Beltrame (Polytechnique de Montreal)
  85. Vector Quantized Replay for Continual Learning, Lucas Caccia (McGIll); Eugene Belilovsky (Mila); Joelle Pineau (McGill / Facebook)
  86. Optimizing Medical Report Generation for Clinical Pertinence, Wisdom E D’Almeida (KIIT )
  87. Quantifying Exposure Bias, Kushal Aora (MILA); Jackie Chi Kit Cheung (McGill University / Mila); Doina Precup (McGill University)
  88. Internal representation dynamics and geometry in recurrent neural networks, Stefan Horoi (Université de Montréal); Guillaume Lajoie (Université de Montréal, Mila); Guy Wolf (Université de Montréal)
  89. TextCode: Temporal Medical Text & Code Representation, Sajad Darabi (University of California Los Angeles)