1 citations · 2 across the 5 of their papers we have counts for
5 papers
Joint Graph Rewiring and Feature Denoising via Spectral Resonance
Jonas Linkerhägner, Cheng Shi, Ivan Dokmanić
When learning from graph data, the graph and the node features both give noisy information about the node labels. In this paper we propose an algorithm to jointly denoise the featu…
Spring-block theory of feature learning in deep neural networks
Cheng Shi, Liming Pan, Ivan Dokmanić
Feature-learning deep nets progressively collapse data to a regular low-dimensional geometry. How this emerges from the collective action of nonlinearity, noise, learning rate, and…
High-Rate Phase Association with Travel Time Neural Fields
Cheng Shi, Giulio Poggiali, Chris Marone +2
Earthquake science and seismology rely on the ability to associate seismic waves with their originating earthquakes. Earthquake detection algorithms based on deep learning have pro…
A Graph Dynamics Prior for Relational Inference
Liming Pan, Cheng Shi, Ivan Dokmanić
Relational inference aims to identify interactions between parts of a dynamical system from the observed dynamics. Current state-of-the-art methods fit the dynamics with a graph ne…
Homophily modulates double descent generalization in graph convolution networks
Cheng Shi, Liming Pan, Hong Hu +1
Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understo…