200 citations · 249 across the 6 of their papers we have counts for
8 papers
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…
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…
Transfer Learning with Binary Neural Networks
Sam Leroux, Steven Bohez, Tim Verbelen +3
Previous work has shown that it is possible to train deep neural networks with low precision weights and activations. In the extreme case it is even possible to constrain the netwo…
Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
Cedric De Boom, Rohan Agrawal, Samantha Hansen +5
The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice…
Decoupled Learning of Environment Characteristics for Safe Exploration
Pieter Van Molle, Tim Verbelen, Steven Bohez +3
Reinforcement learning is a proven technique for an agent to learn a task. However, when learning a task using reinforcement learning, the agent cannot distinguish the characterist…
Representation learning for very short texts using weighted word embedding aggregation
Cedric De Boom, Steven Van Canneyt, Thomas Demeester +1
Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic…