Portrait of Rim Assouel is unavailable

Rim Assouel

assouelr@mila.quebec
PhD - Université de Montréal
Supervisor
Yoshua Bengio

Publications

The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Sai Rajeswar
Hector Palacios
Quentin Cappart
David Vazquez
Nicolas Chapados
Maxime Gasse
Alexandre Lacoste
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (published)
openreview.net
openreview.net
OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning
Rim Assouel
Pau Rodriguez
Perouz Taslakian
David Vazquez
Yoshua Bengio
2023-10-28
ArXiv (preprint)
doi.org
arxiv.org
OCIM : Object-centric Compositional Imagination for Visual Abstract Reasoning
Rim Assouel
Pau Rodriguez
Perouz Taslakian
David Vazquez
Yoshua Bengio
A long-sought property of machine learning systems is the ability to compose learned concepts in novel ways that would enable them to m… (see more)ake sense of new situations. Such capacity for imagination -- a core aspect of human intelligence -- is not yet attained for machines. In this work, we show that object-centric inductive biases can be leveraged to derive an imagination-based learning framework that achieves compositional generalization on a series of tasks. Our method, denoted Object-centric Compositional IMagination (OCIM), decomposes visual reasoning tasks into a series of primitives applied to objects without using a domain-specific language. We show that these primitives can be recomposed to generate new imaginary tasks. By training on such imagined tasks, the model learns to reuse the previously-learned concepts to systematically generalize at test time. We test our model on a series of arithmetic tasks where the model has to infer the sequence of operations (programs) applied to a series of inputs. We find that imagination is key for the model to find the correct solution for unseen combinations of operations.
2023-02-01
ICLR.cc/2023/Conference (rejected)
openreview.net
openreview.net
VIM: Variational Independent Modules for Video Prediction
Rim Assouel
Lluis Castrejon
Aaron Courville
Nicolas Ballas
Yoshua Bengio
We introduce a variational inference model called VIM, for Variational Independent Modules, for sequential data that learns and infers laten… (see more)t representations as a set of objects and discovers modular causal mechanisms over these objects. These mechanisms - which we call modules - are independently parametrized, define the stochastic transitions of entities and are shared across entities. At each time step, our model infers from a low-level input sequence a high-level sequence of categorical latent variables to select which transition modules to apply to which high-level object. We evaluate this model in video prediction tasks where the goal is to predict multi-modal future events given previous observations. We demonstrate empirically that VIM can model 2D visual sequences in an interpretable way and is able to identify the underlying dynamically instantiated mechanisms of the generation process. We additionally show that the learnt modules can be composed at test time to generalize to out-of-distribution observations.
2022-06-28
Proceedings of the First Conference on Causal Learning and Reasoning (published)
proceedings.mlr.press
openreview.net