Sarthak Mittal

PhD - Université de Montréal

Supervisor

Guillaume Lajoie

Co-supervisor

Yoshua Bengio

Research Topics

Deep Learning

Generative Models

Probabilistic Models

Website

Google Scholar

GitHub

Blog Posts

June 13, 2022

Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal

Read the article

Publications

Is a Modular Architecture Enough?

Sarthak Mittal

Yoshua Bengio

Guillaume Lajoie

Inspired from human cognition, machine learning systems are gradually revealing advantages of sparser and more modular architectures. Recent… (see more) work demonstrates that not only do some modular architectures generalize well, but they also lead to better out of distribution generalization, scaling properties, learning speed, and interpretability. A key intuition behind the success of such systems is that the data generating system for most real-world settings is considered to consist of sparse modular connections, and endowing models with similar inductive biases will be helpful. However, the field has been lacking in a rigorous quantitative assessment of such systems because these real-world data distributions are complex and unknown. In this work, we provide a thorough assessment of common modular architectures, through the lens of simple and known modular data distributions. We highlight the benefits of modularity and sparsity and reveal insights on the challenges faced while optimizing modular systems. In doing so, we propose evaluation metrics that highlight the benefits of modularity, the regimes in which these benefits are substantial, as well as the sub-optimality of current end-to-end learned modular systems as opposed to their claimed potential.

openreview.net

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Nan Rosemary Ke

Aniket Rajiv Didolkar

Danilo Jimenez Rezende

Yoshua Bengio

Michael Curtis Mozer

Chris Pal

Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise tha… (see more)t the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.

2021-10-11

NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (published)

openreview.net