Publication

What graph neural networks cannot learn: depth vs width

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

What graph neural networks cannot learn: depth vs width

Graph Chatbot

Chat with Graph Search

Graph generative deep learning models with an application to circuit topologies

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al.

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Optimizing Dynamic Aperture Studies with Active Learning

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks