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Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from ra…
CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai +5
We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demons…
Towards Sparse Hierarchical Graph Classifiers
Cătălina Cangea, Petar Veličković, Nikola Jovanović +2
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. Wh…
Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang +2
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which ca…
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite…