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06/06/2023
CIN++: Enhancing Topological Message Passing
Graph Neural Networks (GNNs) have demonstrated remarkable success in lea...
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01/23/2023
On the Expressive Power of Geometric Graph Neural Networks
The expressive power of Graph Neural Networks (GNNs) has been studied ex...
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06/17/2022
Sheaf Neural Networks with Connection Laplacians
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) tha...
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04/20/2022
Simplicial Attention Networks
Graph representation learning methods have mostly been limited to the mo...
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02/09/2022
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Cellular sheaves equip graphs with "geometrical" structure by assigning ...
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06/23/2021
Weisfeiler and Lehman Go Cellular: CW Networks
Graph Neural Networks (GNNs) are limited in their expressive power, stru...
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03/04/2021
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predomin...
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11/14/2020
A Geometric Perspective on Self-Supervised Policy Adaptation
One of the most challenging aspects of real-world reinforcement learning...
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09/30/2020
The Role of Isomorphism Classes in Multi-Relational Datasets
Multi-interaction systems abound in nature, from colloidal suspensions t...
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06/12/2020
On Second Order Behaviour in Augmented Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a new class of models...
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02/10/2020
Deep Graph Mapper: Seeing Graphs through the Neural Lens
Recent advancements in graph representation learning have led to the eme...
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10/01/2019
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The distributional perspective on reinforcement learning (RL) has given ...
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06/24/2019
Proximal Distilled Evolutionary Reinforcement Learning
Reinforcement Learning (RL) has recently achieved tremendous success due...
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05/02/2018
