37 citations · 47 across the 9 of their papers we have counts for
11 papers
FeaTok: Feature-Loss Tokenization for Image Generation
Zhang Xiaofeng, Samuel Lavoie, Nicolas Beltran-Velez +6
Discrete image generators learn the distribution of tokens produced by an image tokenizer. These token representations should be easy for generators to model, not merely enable fai…
Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents
Johan Obando-Ceron, Walter Mayor, Samuel Lavoie +3
Recent works have proposed accelerating the wall-clock training time of actor-critic methods via the use of large-scale environment parallelization; unfortunately, these can someti…
Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
Samuel Lavoie, Michael Noukhovitch, Aaron Courville
We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their input conditioning. This paper investigates the representation us…
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…
Modeling Caption Diversity in Contrastive Vision-Language Pretraining
Samuel Lavoie, Polina Kirichenko, Mark Ibrahim +4
There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting h…
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision
Ankit Vani, Bac Nguyen, Samuel Lavoie +2
Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under dis…
Language Model Alignment with Elastic Reset
Michael Noukhovitch, Samuel Lavoie, Florian Strub +1
Finetuning language models with reinforcement learning (RL), e.g. from human feedback (HF), is a prominent method for alignment. But optimizing against a reward model can improve o…
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Yi Ren, Samuel Lavoie, Mikhail Galkin +2
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of resear…
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation
Florian Bordes, Samuel Lavoie, Randall Balestriero +2
Self-Supervised Learning (SSL) models rely on a pretext task to learn representations. Because this pretext task differs from the downstream tasks used to evaluate the performance…
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
Samuel Lavoie, Christos Tsirigotis, Max Schwarzer +4
Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into simplices of dimensions each usin…
Serverless architecture efficiency: an exploratory study
Samuel Lavoie, Anthony Garant, Fabio Petrillo
Cloud service provider propose services to insensitive customers to use their platform. Different services can achieve the same result at different cost. In this paper, we study th…