200 citations · 557 across the 42 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…