ServiceNow Research

Issam H. Laradji

Issam H. Laradji

Research Scientist

Low Data Learning

Issam Laradji is a research scientist at ServiceNow Research who focuses on methods that minimize the amount of labels required to efficiently train machine learning models. He completed his postdoc at McGill’s Graphics lab and completed his PhD at the Machine Learning lab of University of British Columbia. His current topics of interest are natural language processing, computer vision (both 2D and 3D), and optimization. On the side, he continuously works on Haven-AI, a toolkit to help people build end-to-end deep learning methods and manage large-scale experiments.

Interests
  • Low Supervision
  • Summarization
  • Text Classification
  • Optimization

Publications

Constraining Representations Yields Models That Know What They Don't Know. João Monteiro, Pau Rodriguez, Pierre-André Noël, Issam H. Laradji, David Vazquez. At International Conference of Learning Representations (ICLR),  2023.

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OCR-VQGAN: Taming Text-within-Image Generation. Juan A. Rodriguez, David Vazquez, Marco Pedersoli, Issam H. Laradji, Pau Rodriguez. At Winter Conference on Applications of Computer Vision (WACV),  2023.

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Constraining Low-level Representations to Define Effective Confidence Scores. João Monteiro, Pau Rodriguez, Pierre-André Noël, Issam H. Laradji, David Vazquez. At Workshop at the Neural Information Processing Systems (NeurIPS),  2022.

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Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation. Sai Rajeswar Mudumba, Issam H. Laradji, Pau Rodriguez, David Vazquez, Aaron Courville. At British Machine Vision Conference (BMVC),  2022.

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OSM: An Open Set Matting Framework with OOD Detection and Few-Shot Learning. Issam H. Laradji, Yuhongze Zhou, Derek Nowrouzezahrai. At British Machine Vision Conference (BMVC),  2022.

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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. Gaurav Sahu, Pau Rodriguez, Issam H. Laradji, Parmida Atighhehchian, David Vazquez, Dzmitry Bahdanau. At Montreal AI Symposium (MAIS),  2022.

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Leveraging Activation Patterns to Define Classifiers Able to Detect and Reject Anomalies. João Monteiro, Pau Rodriguez, Pierre-André Noël, Issam H. Laradji, David Vazquez. At Montreal AI Symposium (MAIS),  2022.

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S-LLM: Semi-Supervised Large Language Model for Chat Summarization. Issam H. Laradji, Sathwik Tejaswi Madhusudhan, Orlando Marquez, Pau Rodriguez, David Vazquez. At Montreal AI Symposium (MAIS),  2022.

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Countering Language Drift with KL Regularization. Issam H. Laradji, Michael Noukhovitch, Aaron Courville. At Workshop on Interactive Learning for Natural Language Processing (NeurIPS Workshop),  2022.

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Competition exacerbates Language Drift. Issam H. Laradji, Michael Noukhovitch. At Machine Learning and the Evolution of Language (JCoLE Workshop),  2022.

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Overcoming challenges in leveraging GANs for few-shot data augmentation. Christopher Beckham, Issam H. Laradji, Pau Rodriguez, David Vazquez, Derek Nowrouzezahrai, Christopher Pal. At Workshop at the Conference on Lifelong Learning Agents (CoLLAs),  2022.

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Kubric: A scalable dataset generator. Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam H. Laradji, Hsueh-Ti (Derek)Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi. At Computer Vision and Pattern Recognition (CVPR),  2022.

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Neural Point Light Fields. Julian Ost, Issam H. Laradji, Alejandro Newell, Yuval Bahat, Felix Heide. At Computer Vision and Pattern Recognition (CVPR),  2022.

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Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. Gaurav Sahu, Pau Rodriguez, Parmida Atighhehchian, Issam H. Laradji, David Vazquez, Dzmitry Bahdanau. At Workshop at the Annual Meetings of the Association for Computational Linguistics (ACL),  2022.

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Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. julie nutini, Issam H. Laradji, Mark Schmidt. At Journal of Machine Learning Research (JMLR),  2022.

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A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia. Keegan Lensink, Fu Jorden Lo, Rachel L Eddy, Marco Law, Issam H. Laradji, Eldad Haber, Savvas Nicolaou, Darra Murphy, William A Parker. At Academic Radiology,  2022.

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A Survey of Self-Supervised and Few-Shot Object Detection. Gabriel Huang, Issam H. Laradji, David Vazquez, Simon Lacoste-Julien, Pau Rodriguez. At IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),  2021.

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A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images. Alzayat Saleh, Issam H. Laradji, Corey Lammie, David Vazquez, Carol A Flavell, Mostafa Rahimi Azghadi. At IEEE Journal of Biomedical and Health Informatics,  2021.

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Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vazquez. At International Conference on Computer Vision (ICCV),  2021.

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SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction. Issam H. Laradji, Pau Rodriguez, David Vazquez, Derek Nowrouzezahrai. At Workshop at the International Conference on Computer Vision (ICCV),  2021.

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Weakly Supervised Underwater Fish Segmentation Using Affinity LCFCN. Issam H. Laradji, Alzayat Saleh, Pau Rodriguez, Derek Nowrouzezahrai, Mostafa Rahimi Azghadi, David Vazquez. At Nature Scientific Reports,  2021.

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Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence. Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lacoste-Julien. At International Conference on Artificial Intelligence and Statistics (AISTATS),  2021.

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A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images. Issam H. Laradji, Pau Rodriguez, Oscar Manas, Keegan Lensink, Marco Law, Lironne Kurzman, David Vazquez, Derek Nowrouzezahrai, William A Parker. At Winter Conference on Applications of Computer Vision (WACV),  2021.

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Learning Data Augmentation with Online Bilevel Optimization for Image Classification. Saypraseuth Mounsaveng, Issam H. Laradji, Ismail Ben Ayed, David Vazquez, Marco Pedersoli. At Winter Conference on Applications of Computer Vision (WACV),  2021.

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Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery. Issam H. Laradji, Pau Rodriguez, Alfredo Kalaitzis, David Vazquez, Ross Young, Ed Davey, Alexandre Lacoste. At Workshop at the Neural Information Processing Systems (NeurIPS),  2020.

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin. At Conference on Neural Information Processing Systems (NeurIPS),  2020.

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Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodriguez, Frederic Branchaud, Parmida Atighhehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vazquez. At Conference on Neural Information Processing Systems (NeurIPS),  2020.

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LOOC: Localize Overlapping Objects with Count Supervision. Issam H. Laradji, Rafael Pardinas, Pau Rodriguez, David Vazquez. At International Conference on Image Processing (ICIP),  2020.

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Proposal-based Instance Segmentation with Point Supervision. Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt. At International Conference on Image Processing (ICIP),  2020.

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CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions. Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam H. Laradji, Davide Maltoni. At Artificial Intelligence Journal,  2020.

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Embedding Propagation: Smoother Manifold for Few-Shot Classification. Pau Rodriguez, Issam H. Laradji, Alexandre Drouin, Alexandre Lacoste. At European Conference on Computer Vision (ECCV),  2020.

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin. At Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR),  2020.

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Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lacoste-Julien. At International Conference on Artificial Intelligence and Statistics (AISTATS),  2020.

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A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Alzayat Saleh, Issam H. Laradji, Dmitry A Konovalov, Michael Bradley, David Vazquez, Marcus Sheaves. At Nature Scientific Reports,  2020.

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Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation. Lironne Kurzman, David Vazquez, Issam H. Laradji. At Women in Machine Learning (WiML),  2019.

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Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien. At Conference on Neural Information Processing Systems (NeurIPS),  2019.

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Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation. Lironne Kurzman, David Vazquez, Issam H. Laradji. At Workshop at the International Conference on Computer Vision (ICCV),  2019.

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Where are the Masks: Instance Segmentation with Image-level Supervision. Issam H. Laradji, David Vazquez, Mark Schmidt. At Britsh Machine Vision Conference (BMVC),  2019.

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Efficient Deep Gaussian Process Models for Variable-Sized Inputs. Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim. At International Joint Conference on Neural Networks (IJCNN),  2019.

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Where are the Blobs: Counting by Localization with Point Supervision. Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt. At European Conference on Computer Vision (ECCV),  2018.

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Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodriguez, Frédéric Branchaud-Charron, Parmida Atighhehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vazquez. At Montreal AI Symposium (MAIS),  2018.

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Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation. Lironne Kurzman, David Vazquez, Issam H. Laradji. At Workshop at the International Conference on Learning Representations (ICLR),  2018.

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Workflow discovery in low data regimes. Amine El Hattami, Issam H. Laradji, Stefania Raimondo, David Vazquez, Pau Rodriguez, Christopher Pal. At Transactions on Machine Learning Research,  2017.

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