23 citations · 31 across the 19 of their papers we have counts for
6 papers · 1 filter
Incremental Learning of Affordances using Markov Logic Networks
George Potter, Gertjan Burghouts, Joris Sijs
Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object af…
Open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols
Gertjan Burghouts, Fieke Hillerström, Erwin Walraven +5
We consider the problem of finding spatial configurations of multiple objects in images, e.g., a mobile inspection robot is tasked to localize abandoned tools on the floor. We defi…
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Miltiadis Kofinas, Boris Knyazev, Yan Zhang +5
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural netwo…
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection
Michail Tsiaousis, Gertjan Burghouts, Fieke Hillerström +1
The dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several acti…
Set Prediction without Imposing Structure as Conditional Density Estimation
David W. Zhang, Gertjan J. Burghouts, Cees G. M. Snoek
Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric…