200 citations · 557 across the 42 of their papers we have counts for
6 papers · 1 filter
Symmetry and Complexity in Object-Centric Deep Active Inference Models
Stefano Ferraro, Toon Van de Maele, Tim Verbelen +1
Humans perceive and interact with hundreds of objects every day. In doing so, they need to employ mental models of these objects and often exploit symmetries in the object's shape…
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models
Stefano Ferraro, Toon Van de Maele, Pietro Mazzaglia +2
Active inference is a first principles approach for understanding the brain in particular, and sentient agents in general, with the single imperative of minimizing free energy. As…
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
Pieter Van Molle, Miguel De Strooper, Tim Verbelen +3
Because of their state-of-the-art performance in computer vision, CNNs are becoming increasingly popular in a variety of fields, including medicine. However, as neural networks are…
Learning to Grasp from a Single Demonstration
Pieter Van Molle, Tim Verbelen, Elias De Coninck +3
Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robo…
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Sam Leroux, Pavlo Molchanov, Pieter Simoens +3
Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be m…
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…