Eugene Belilovsky

Paul Janson

PhD - Concordia University

Co-supervisor :

Charles-Etienne Joseph

Master's Research - Université de Montréal

Co-supervisor :

Zafir Khalid

Master's Research - Concordia University

Co-supervisor :

Irina Rish

Website

Gwen Legate

PhD - Concordia University

Co-supervisor :

Master's Research - Concordia University

Co-supervisor :

Melika Minaei Bidgoli

Research Intern - Concordia University University

Abhinav Moudgil

PhD - Concordia University

Website

Google Scholar

Adel Nabli

PhD - Concordia University

Google Scholar

Geraldin Nanfack

Postdoctorate - Concordia University

Co-supervisor :

geraldin.nanfack@mila.quebec

Website

Google Scholar

Albert Orozco Camacho

PhD - Concordia University

Co-supervisor :

PhD - Concordia University

Co-supervisor :

Irina Rish

Benjamin Therien

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Principal supervisor :

Master's Research - Concordia University

AmirHossein Zamani

PhD - Concordia University

Co-supervisor :

Congshu Zou

Master's Research - Concordia University

Publications

Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Boris Knyazev

Harm de Vries

Cătălina Cangea

Graham W. Taylor

Aaron Courville

2020-05-17

ArXiv (preprint)

Online Continual Learning with Maximally Interfered Retrieval

Rahaf Aljundi

Lucas Caccia

Massimo Caccia

Min Lin

Laurent Charlin

Tinne Tuytelaars

Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for mo… (see more)dern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.

2019-08-11

ArXiv (preprint)

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Cătălina Cangea

Pietro Lio

Aaron Courville

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely o… (see more)n its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.

2019-01-01

ViGIL@NeurIPS (published)

Blindfold Baselines for Embodied QA

Ankesh Anand

Kyle Kastner

Hugo Larochelle

Aaron Courville

We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question b… (see more)y intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.

2018-11-12

ArXiv (preprint)

Learning Optimizers for Local SGD

Charles-Étienne Joseph

Benjamin Thérien

Abhinav Moudgil

Boris Knyazev