17 citations · 58 across the 13 of their papers we have counts for
13 papers
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
Lexiang Tang, Weihao Gao, Bingchen Zhao +4
Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However,…
Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience
Jiangjie Chen, Wenxiang Chen, Jiacheng Du +19
Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast, utilizing LLMs for theorem proving in formal languages (such as…
MolSculpt: Sculpting 3D Molecular Geometries from Chemical Syntax
Zhanpeng Chen, Weihao Gao, Shunyu Wang +3
Generating precise 3D molecular geometries is crucial for drug discovery and material science. While prior efforts leverage 1D representations like SELFIES to ensure molecular vali…
OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue
Weihao Gao, Zhuo Deng, Zhiyuan Niu +10
Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and genera…
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
Supervised Pretraining for Molecular Force Fields and Properties Prediction
Xiang Gao, Weihao Gao, Wenzhi Xiao +3
Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods su…
Learning Regularized Positional Encoding for Molecular Prediction
Xiang Gao, Weihao Gao, Wenzhi Xiao +3
Machine learning has become a promising approach for molecular modeling. Positional quantities, such as interatomic distances and bond angles, play a crucial role in molecule physi…
Learning to Simulate Unseen Physical Systems with Graph Neural Networks
Ce Yang, Weihao Gao, Di Wu +1
Simulation of the dynamics of physical systems is essential to the development of both science and engineering. Recently there is an increasing interest in learning to simulate the…
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks
Tianze Zheng, Weihao Gao, Chong Wang
Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator. Due to physical constraints, the time step of…
Defending against Reconstruction Attack in Vertical Federated Learning
Jiankai Sun, Yuanshun Yao, Weihao Gao +2
Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared…
Vertical Federated Learning without Revealing Intersection Membership
Jiankai Sun, Xin Yang, Yuanshun Yao +4
Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g. features and labels) of the same data entity (e.g. a person) to jointly train a model.…
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong, Xiaojie Jin, Weihao Gao +5
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training…
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
Weihao Gao, Xiangjun Fan, Chong Wang +7
One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mo…