4 papers
Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models
Thibaut Kulak
Recent progress in large-scale sequence modeling has shown that a single model can learn useful representations across highly diverse data distributions. Inspired by these advances…
A Bayesian Variational principle for dynamic Self Organizing Maps
Anthony Fillion, Thibaut Kulak, François Blayo
We propose organisation conditions that yield a method for training SOM with adaptative neighborhood radius in a variational Bayesian framework. This method is validated on a non-s…
A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)
Thibaut Kulak, Anthony Fillion, François Blayo
We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be deriv…
Representation Learning in Partially Observable Environments using Sensorimotor Prediction
Thibaut Kulak, Michael Garcia Ortiz
In order to explore and act autonomously in an environment, an agent needs to learn from the sensorimotor information that is captured while acting. By extracting the regularities…