research
∙
06/16/2023
Power-law Dynamic arising from machine learning
We study a kind of new SDE that was arisen from the research on optimiza...
share
research
∙
04/07/2023
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in m...
research
∙
10/24/2022
Structure-based Drug Design with Equivariant Diffusion Models
Structure-based drug design (SBDD) aims to design small-molecule ligands...
research
∙
06/17/2022
A Flexible Diffusion Model
Diffusion (score-based) generative models have been widely used for mode...
research
∙
10/26/2021
Equivariant vector field network for many-body system modeling
Modeling many-body systems has been a long-standing challenge in science...
research
∙
03/03/2021
Wide Graph Neural Networks: Aggregation Provably Leads to Exponentially Trainability Loss
Graph convolutional networks (GCNs) and their variants have achieved gre...
research
∙
11/25/2020
Implicit bias of deep linear networks in the large learning rate phase
Correctly choosing a learning rate (scheme) for gradient-based optimizat...
research
∙
04/13/2020
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
In recent years, a critical initialization scheme with orthogonal initia...
research
∙
12/19/2019