Marc T. Law

Marc T. Law NVIDIA Toronto AI Lab 431 King St W, 6th floor, Toronto, ON M5V 3M4 Canada

Introduction	I am a senior research scientist at NVIDIA working on machine learning and computer vision. I am a member of the NVIDIA Toronto AI Lab (Canada) led by Professor Sanja Fidler. I received a PhD in Computer Science from Université Pierre et Marie Curie (now Sorbonne Université in Paris, France) in 2015. My doctoral supervisors were Professor Matthieu Cord and Professor Stéphane Gançarski, I was also supervised by Professor Nicolas Thome. I was a visiting research scholar in the team of Professor Eric Xing at the School of Computer Science, Carnegie Mellon University in 2015~2016. From 2016 to 2019, I was a postdoctoral fellow in the Department of Computer Science (Machine Learning group) at the University of Toronto and Vector Institute under the supervision of Professor Raquel Urtasun and Professor Richard Zemel.
Research Interest	My main focus is to propose scalable machine learning methods that can be applied to computer vision tasks. More specifically, my domains of interest are: distance metric learning, complex representations including hierarchies and graphs, differential geometry, algebraic topology, convex and non-convex optimization, optimization on manifolds, information geometry, structured output prediction, generalization with limited supervision including few-shot learning, semi-supervised learning, active learning, domain adaptation and self-supervised learning. Graduate and senior (third- and fourth-year) undergraduate students interested in doing an internship in the NVIDIA Toronto AI Lab can directly send me their resume.

Publications	International Conferences Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting Viraj Prabhu, David Acuna, Rafid Mahmood, Marc T. Law, Yuan-Hong Liao, Judy Hoffman, Sanja Fidler, James Lucas Transactions on Machine Learning Research (TMLR) 2023 [pdf] Spacetime Representation Learning Marc T. Law, James Lucas International Conference on Learning Representations (ICLR) 2023 [pdf] Optimizing Data Collection for Machine Learning Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law Neural Information Processing Systems (NeurIPS) 2022 [pdf] [supp. material] How Much More Data Do I Need? Estimating Requirements for Downstream Tasks Rafid Mahmood, James Lucas, David Acuna, Daiqing Li, Jonah Philion, Jose M. Alvarez, Zhiding Yu, Sanja Fidler, Marc T. Law Conference on Computer Vision and Pattern Recognition (CVPR) 2022 [pdf] [supp. material] Domain Adversarial Training: A Game Perspective David Acuna, Marc T. Law, Guojun Zhang, Sanja Fidler International Conference on Learning Representations (ICLR) 2022 [pdf] Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach Rafid Mahmood, Sanja Fidler, Marc T. Law International Conference on Learning Representations (ICLR) 2022 [pdf] Ultrahyperbolic Neural Networks Marc T. Law Neural Information Processing Systems (NeurIPS) 2021 [pdf] Self-Supervised Real-to-Sim Scene Generation Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Stan Birchfield, Marc T. Law International Conference on Computer Vision (ICCV) 2021 [pdf] [project page] f-Domain Adversarial Learning: Theory and Algorithms David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler International Conference on Machine Learning (ICML) 2021 [pdf] Ultrahyperbolic Representation Learning Marc T. Law, Jos Stam Neural Information Processing Systems (NeurIPS) 2020 [pdf] [code] A Theoretical Analysis of the Number of Shots in Few-Shot Learning Tianshi Cao, Marc T. Law, Sanja Fidler International Conference on Learning Representations (ICLR) 2020 [pdf] Video Face Clustering with Unknown Number of Clusters Makarand Tapaswi, Marc T. Law, Sanja Fidler International Conference on Computer Vision (ICCV) 2019 [pdf] [code] Lorentzian Distance Learning for Hyperbolic Representations Marc T. Law, Renjie Liao, Jake Snell, Richard S. Zemel International Conference on Machine Learning (ICML) 2019 [pdf] [code: Retrieval / Binary classification] Dimensionality Reduction for Representing the Knowledge of Probabilistic Models Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel International Conference on Learning Representations (ICLR) 2019 [pdf] [code: Visualization / Zero-shot learning 1 2] Centroid-based Deep Metric Learning for Speaker Recognition Jixuan Wang, Kuan-Chieh Wang, Marc T. Law, Frank Rudzicz, Michael Brudno International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019 [pdf] Representing Relative Visual Attributes with a Reference-Point-Based Decision Model Marc T. Law, Paul Weng International Conference on Pattern Recognition (ICPR) 2018 [pdf] Deep Spectral Clustering Learning Marc T. Law, Raquel Urtasun, Richard S. Zemel International Conference on Machine Learning (ICML) 2017 [pdf] [supp. material] [project page] Efficient Multiple Instance Metric Learning using Weakly Supervised Data Marc T. Law, Yiaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing Conference on Computer Vision and Pattern Recognition (CVPR) 2017 [pdf] [code] Closed-Form Training of Mahalanobis Distance for Supervised Clustering Marc T. Law, Yiaoliang Yu, Matthieu Cord, Eric P. Xing Conference on Computer Vision and Pattern Recognition (CVPR) 2016 [pdf] [code] Fantope Regularization in Metric Learning Marc T. Law, Nicolas Thome, Matthieu Cord Conference on Computer Vision and Pattern Recognition (CVPR) 2014 [doi] [pdf] [code] Quadruplet-wise Image Similarity Learning Marc T. Law, Nicolas Thome, Matthieu Cord International Conference on Computer Vision (ICCV) 2013 [doi] [pdf] Structural and Visual Comparisons for Web Page Archiving Marc T. Law, Nicolas Thome, Stéphane Gançarski, Matthieu Cord ACM Symposium on Document Engineering (DocEng) 2012 [doi] [pdf] [code] [demo webpage (thanks to Andres Sanoja and Zeynep Pehlivan)] International Journal Learning a Distance Metric from Relative Comparisons between Quadruplets of Images Marc T. Law, Nicolas Thome, Matthieu Cord International Journal of Computer Vision (IJCV) 2017 International Workshops Hybrid Pooling Fusion in the BoW Pipeline Marc T. Law, Nicolas Thome, Matthieu Cord ECCV 2012 Workshop on Information fusion in Computer Vision for Concept Recognition (ECCV-IFCVCR 2012) [pdf] Structural and Visual Similarity Learning for Web Page Archiving Marc T. Law, Carlos Sureda Gutierrez, Nicolas Thome, Stéphane Gançarski, Matthieu Cord 10th workshop on Content-Based Multimedia Indexing (CBMI) 2012 [pdf] Book Chapter Bag-of-Words Image Representation: Key Ideas and Further Insight Marc T. Law, Nicolas Thome, Matthieu Cord Fusion in Computer Vision - Understanding Complex Visual Content, Springer 2014 National Conferences Feature Generation for Long-tail Classification Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian, Makarand Tapaswi Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2021 [pdf] [code] Apprentissage de métrique appliqué à la détection de changement de page Web Marc T. Law, Nicolas Thome, Stéphane Gançarski, Matthieu Cord CORIA 2015 Patents Optimized active learning using integer programming Rafid Mahmood, Sanja Fidler, Marc T. Law US Patent App. 17/591,039, 2023 Processing Ultrahyperbolic Representations using Neural Networks Marc T. Law US Patent App. 17/827,132, 2022 Domain Adaptation using Domain-Adversarial Learning in Synthetic Data Systems and Applications David Acuna, Sanja Fidler, Marc T. Law, Guojun Zhang US Patent App. 17/827,141, 2022 Unsupervised Domain Adaptation with Neural Networks David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler US Patent App. 17/226,534, 2021 Scene graph generation for unlabeled data Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Marc T. Law US Patent 11,574,155, 2021 PhD Thesis Distance Metric Learning for Image and Webpage Comparison Université Pierre et Marie Curie (Paris 6), 2015 [pdf] [hal]


Teaching (Moniteur)	University Paris 6, Licence 2, LI214: Logic and Automata. (38.5 hours) University Paris 6, Licence 2, LI215: Imperative programming and data structures in C. (38.5 hours) University Paris 6, Licence 2, LI230: Object-oriented Programming in Java. (110 hours) University Paris 6, Master 1 Artificial Intelligence: Project advisor. (3.5 hours) University Paris 6/Telecom ParisTech, Master 2 Image Processing: Project advisor. (4 hours)