research
∙
04/06/2023
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching
Cross-device user matching is a critical problem in numerous domains, in...
share
research
∙
03/06/2023
Graph Positional Encoding via Random Feature Propagation
Two main families of node feature augmentation schemes have been explore...
research
∙
02/22/2023
Equivariant Polynomials for Graph Neural Networks
Graph Neural Networks (GNN) are inherently limited in their expressive p...
research
∙
01/30/2023
Equivariant Architectures for Learning in Deep Weight Spaces
Designing machine learning architectures for processing neural networks ...
research
∙
10/28/2022
Generalized Laplacian Positional Encoding for Graph Representation Learning
Graph neural networks (GNNs) are the primary tool for processing graph-s...
research
∙
06/22/2022
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Subgraph GNNs are a recent class of expressive Graph Neural Networks (GN...
research
∙
04/18/2022
Optimizing Tensor Network Contraction Using Reinforcement Learning
Quantum Computing (QC) stands to revolutionize computing, but is current...
research
∙
03/02/2022
A Simple and Universal Rotation Equivariant Point-cloud Network
Equivariance to permutations and rigid motions is an important inductive...
research
∙
02/02/2022
Multi-Task Learning as a Bargaining Game
In Multi-task learning (MTL), a joint model is trained to simultaneously...
research
∙
01/20/2022
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Standard Federated Learning (FL) techniques are limited to clients with ...
research
∙
12/18/2021
Weisfeiler and Leman go Machine Learning: The Story so far
In recent years, algorithms and neural architectures based on the Weisfe...
research
∙
10/06/2021
Equivariant Subgraph Aggregation Networks
Message-passing neural networks (MPNNs) are the leading architecture for...
research
∙
08/02/2021
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Can a generative model be trained to produce images from a specific doma...
research
∙
04/14/2021
Deep Permutation Equivariant Structure from Motion
Existing deep methods produce highly accurate 3D reconstructions in ster...
research
∙
10/22/2020
Position-Agnostic Multi-Microphone Speech Dereverberation
Neural networks (NNs) have been widely applied in speech processing task...
research
∙
10/17/2020
On Size Generalization in Graph Neural Networks
Graph neural networks (GNNs) can process graphs of different sizes but t...
research
∙
10/11/2020
How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
We consider the problem of monitoring and controlling a partially-observ...
research
∙
10/06/2020
On the Universality of Rotation Equivariant Point Cloud Networks
Learning functions on point clouds has applications in many fields, incl...
research
∙
03/29/2020
Self-Supervised Learning for Domain Adaptation on Point-Clouds
Self-supervised learning (SSL) allows to learn useful representations fr...
research
∙
03/12/2020
Learning Algebraic Multigrid Using Graph Neural Networks
Efficient numerical solvers for sparse linear systems are crucial in sci...
research
∙
02/20/2020
Set2Graph: Learning Graphs From Sets
Many problems in machine learning (ML) can be cast as learning functions...
research
∙
02/20/2020
On Learning Sets of Symmetric Elements
Learning from unordered sets is a fundamental learning setup, which is a...
research
∙
05/28/2019
Controlling Neural Level Sets
The level sets of neural networks represent fundamental properties such ...
research
∙
05/27/2019
Provably Powerful Graph Networks
Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to ...
research
∙
01/27/2019
On the Universality of Invariant Networks
Constraining linear layers in neural networks to respect symmetry transf...
research
∙
12/27/2018
Surface Networks via General Covers
Developing deep learning techniques for geometric data is an active and ...
research
∙
12/24/2018
Invariant and Equivariant Graph Networks
Invariant and equivariant networks have been successfully used for learn...
research
∙
06/06/2018
Multi-chart Generative Surface Modeling
This paper introduces a 3D shape generative model based on deep neural n...
research
∙
03/27/2018
Point Convolutional Neural Networks by Extension Operators
This paper presents Point Convolutional Neural Networks (PCNN): a novel ...
research
∙
05/17/2017