10 citations · 15 across the 4 of their papers we have counts for
4 papers
Learning Generative Models for Active Inference using Tensor Networks
Samuel T. Wauthier, Bram Vanhecke, Tim Verbelen +1
Active inference provides a general framework for behavior and learning in autonomous agents. It states that an agent will attempt to minimize its variational free energy, defined…
A learning gap between neuroscience and reinforcement learning
Samuel T. Wauthier, Pietro Mazzaglia, Ozan Çatal +3
Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel advances in the field. However, current progress in reinforcement learning is largely foc…
Deep Active Inference for Autonomous Robot Navigation
Ozan Çatal, Samuel Wauthier, Tim Verbelen +2
Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is…
Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization
Cedric De Boom, Samuel Wauthier, Tim Verbelen +1
When designing variational autoencoders (VAEs) or other types of latent space models, the dimensionality of the latent space is typically defined upfront. In this process, it is po…