200 citations · 557 across the 42 of their papers we have counts for
5 papers · 1 filter
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…
Learning Perception and Planning with Deep Active Inference
Ozan Çatal, Tim Verbelen, Johannes Nauta +2
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiment…
Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks
Cedric De Boom, Stephanie Van Laere, Tim Verbelen +1
Music that is generated by recurrent neural networks often lacks a sense of direction and coherence. We therefore propose a two-stage LSTM-based model for lead sheet generation, in…
Learning to Catch Piglets in Flight
Ozan Çatal, Lawrence De Mol, Tim Verbelen +1
Catching objects in-flight is an outstanding challenge in robotics. In this paper, we present a closed-loop control system fusing data from two sensor modalities: an RGB-D camera a…