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cs.LG2026
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…
cs.LG2022
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…
cs.LG2022
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…