23 citations · 23 across the 3 of their papers we have counts for
5 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…
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…
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…
Lazy Evaluation of Convolutional Filters
Sam Leroux, Steven Bohez, Cedric De Boom +5
In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network. This allows to trade-off the accuracy of a deep neural n…
Efficiency Evaluation of Character-level RNN Training Schedules
Cedric De Boom, Sam Leroux, Steven Bohez +3
We present four training and prediction schedules from the same character-level recurrent neural network. The efficiency of these schedules is tested in terms of model effectivenes…