200 citations · 557 across the 42 of their papers we have counts for
12 papers · 1 filter
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…
Home Run: Finding Your Way Home by Imagining Trajectories
Daria de Tinguy, Pietro Mazzaglia, Tim Verbelen +1
When studying unconstrained behaviour and allowing mice to leave their cage to navigate a complex labyrinth, the mice exhibit foraging behaviour in the labyrinth searching for rewa…
The Free Energy Principle for Perception and Action: A Deep Learning Perspective
Pietro Mazzaglia, Tim Verbelen, Ozan Çatal +1
The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states…
Contrastive Active Inference
Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt
Active inference is a unifying theory for perception and action resting upon the idea that the brain maintains an internal model of the world by minimizing free energy. From a beha…
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…
Curiosity-Driven Exploration via Latent Bayesian Surprise
Pietro Mazzaglia, Ozan Catal, Tim Verbelen +1
The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in the process of skill acquisition. With the aid of artificial curiosity, we could…
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…
Bayesian policy selection using active inference
Ozan Çatal, Johannes Nauta, Tim Verbelen +2
Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a we…
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations
Xander Steenbrugge, Sam Leroux, Tim Verbelen +1
In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE's to learn a useful latent space on a set of relat…
Privacy Aware Offloading of Deep Neural Networks
Sam Leroux, Tim Verbelen, Pieter Simoens +1
Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices. Offloading the computations…
Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes
Cedric De Boom, Thomas Demeester, Bart Dhoedt
Recurrent neural networks are nowadays successfully used in an abundance of applications, going from text, speech and image processing to recommender systems. Backpropagation throu…