121 citations · 295 across the 14 of their papers we have counts for
8 papers · 1 filter
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Shengchao Liu, Weitao Du, Yanjing Li +10
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of…
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Shengchao Liu, Weitao Du, Zhiming Ma +2
Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D…
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Weitao Du, Yuanqi Du, Limei Wang +5
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a co…
A Flexible Diffusion Model
Weitao Du, Tao Yang, He Zhang +1
Diffusion (score-based) generative models have been widely used for modeling various types of complex data, including images, audios, and point clouds. Recently, the deep connectio…
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Wei Huang, Yayong Li, Weitao Du +4
Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from…
Implicit bias of deep linear networks in the large learning rate phase
Wei Huang, Weitao Du, Richard Yi Da Xu +1
Most theoretical studies explaining the regularization effect in deep learning have only focused on gradient descent with a sufficient small learning rate or even gradient flow (in…
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
Wei Huang, Weitao Du, Richard Yi Da Xu
The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training. The increase in learning speed that results from orthogonal…
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply
Wei Huang, Richard Yi Da Xu, Weitao Du +2
In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural netw…