Search Results for author: Simon Brodeur

Found 7 papers, 1 papers with code

Self-Supervised Learning of Motion-Informed Latents

no code implementations29 Sep 2021 Raphaël Jean, Pierre-Luc St-Charles, Soren Pirk, Simon Brodeur

Our goal is to show that common Siamese networks can effectively be trained on video sequences to disentangle attributes related to pose and motion that are useful for video and non-video tasks, yet typically suppressed in usual training schemes.

Action Recognition Data Augmentation +2

AriEL: volume coding for sentence generation

no code implementations30 Mar 2020 Luca Celotti, Simon Brodeur, Jean Rouat

This partially supports to the hypothesis that encoding information into volumes instead of into points, can lead to improved retrieval of learned information with random sampling.

Language coverage and generalization in RNN-based continuous sentence embeddings for interacting agents

no code implementations5 Nov 2019 Luca Celotti, Simon Brodeur, Jean Rouat

While it is known that those embeddings are able to learn some structures of language (e. g. grammar) in a purely data-driven manner, there is very little work on the objective evaluation of their ability to cover the whole language space and to generalize to sentences outside the language bias of the training data.

Dialogue Generation Sentence Embeddings

Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir

no code implementations27 Apr 2018 Marc-Antoine Moinnereau, Thomas Brienne, Simon Brodeur, Jean Rouat, Kevin Whittingstall, Eric Plourde

The use of electroencephalogram (EEG) as the main input signal in brain-machine interfaces has been widely proposed due to the non-invasive nature of the EEG.

EEG General Classification

HoME: a Household Multimodal Environment

no code implementations29 Nov 2017 Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context.

OpenAI Gym reinforcement-learning

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