no code implementations • 1 Dec 2023 • Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou, Guy Wolf
This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts.
no code implementations • 28 Sep 2023 • Qiao Gu, Alihusein Kuwajerwala, Sacha Morin, Krishna Murthy Jatavallabhula, Bipasha Sen, Aditya Agarwal, Corban Rivera, William Paul, Kirsty Ellis, Rama Chellappa, Chuang Gan, Celso Miguel de Melo, Joshua B. Tenenbaum, Antonio Torralba, Florian Shkurti, Liam Paull
We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts.
2 code implementations • 7 Apr 2023 • Sacha Morin, Robin Legault, Félix Laliberté, Zsuzsa Bakk, Charles-Édouard Giguère, Roxane de la Sablonnière, Éric Lacourse
StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes).
no code implementations • 7 Mar 2023 • Sacha Morin, Miguel Saavedra-Ruiz, Liam Paull
A fundamental task in robotics is to navigate between two locations.
no code implementations • 7 Mar 2022 • Miguel Saavedra-Ruiz, Sacha Morin, Liam Paull
In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images.
no code implementations • 14 Jul 2020 • Andrés F. Duque, Sacha Morin, Guy Wolf, Kevin R. Moon
Our regularization, based on the diffusion potential distances from the recently-proposed PHATE visualization method, encourages the learned latent representation to follow intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and reconstruction of data in the original feature space from latent coordinates.