Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript. - Fangcheng Fu; Yuzheng Hu; Yihan He; Jiawei Jiang 001; Yingxia Shao; Ce Zhang 001; Bin Cui 001 (ICML, 2020)

The Open Catalyst Challenge 2021: Competition Report. - Abhishek Das; Muhammed Shuaibi; Aini Palizhati; Siddharth Goyal; Aditya Grover; Adeesh Kolluru; Janice Lan; Ammar Rizvi; Anuroop Sriram; Brandon M. Wood; Devi Parikh; Zachary W. Ulissi; et al. (NeurIPS (Competition and Demos), 2021)

DPGraph: A Benchmark Platform for Differentially Private Graph Analysis. - Siyuan Xia; Beizhen Chang; Karl Knopf; Yihan He; Yuchao Tao; Xi He 001 (SIGMOD Conference, 2021)

Achievability and Impossibility of Exact Pairwise Ranking. - Yihan He (CoRR, 2021)

PAC-Learning Uniform Ergodic Communicative Networks. - Yihan He (CoRR, 2021)

Multi-fidelity Stability for Graph Representation Learning. - Yihan He; Joan Bruna (CoRR, 2021)

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles. - Chejian Xu; Wenhao Ding; Weijie Lyu; Zuxin Liu; Shuai Wang; Yihan He; Hanjiang Hu; Ding Zhao; Bo Li 026 (NeurIPS, 2022)

Probabilistic-Bits based on Ferroelectric Field-Effect Transistors for Stochastic Computing. - Sheng Luo; Yihan He; Baofang Cai; Xiao Gong; Gengchiau Liang (CoRR, 2023)

Ground-State Probabilistic Logic with the Simplest Binary Energy Landscape for Probabilistic Computing. - Yihan He; Sheng Luo; Chao Fang; Gengchiau Liang (CoRR, 2023)

Few Samples Radar Signal Recognition based on Conditional Encoding Generative Adversarial Network Model. - Yihan He; Yanping Liao; Qiang Guo (ICCT, 2024)

Global Convergence in Training Large-Scale Transformers. - Cheng Gao; Yuan Cao; Zihao Li; Yihan He; Mengdi Wang; Han Liu; Jason M. Klusowski; Jianqing Fan (NeurIPS, 2024)

One-Layer Transformer Provably Learns One-Nearest Neighbor In Context. - Zihao Li; Yuan Cao; Cheng Gao; Yihan He; Han Liu; Jason M. Klusowski; Jianqing Fan; Mengdi Wang (NeurIPS, 2024)

A Universal Representation Mechanism for Multisource Remote Sensing Image. - Weili Kong; Baisen Liu; Xiaojun Bi 002; Yihan He (IEEE Geosci. Remote. Sens. Lett., 2024)

Learning Spectral Methods by Transformers. - Yihan He; Yuan Cao; Hong-Yu Chen; Dennis Wu; Jianqing Fan; Han Liu (CoRR, 2025)

Transformers Simulate MLE for Sequence Generation in Bayesian Networks. - Yuan Cao; Yihan He; Dennis Wu; Hong-Yu Chen; Jianqing Fan; Han Liu (CoRR, 2025)

Transformers and Their Roles as Time Series Foundation Models. - Dennis Wu; Yihan He; Yuan Cao; Jianqing Fan; Han Liu (CoRR, 2025)

Transformers versus the EM Algorithm in Multi-class Clustering. - Yihan He; Hong-Yu Chen; Yuan Cao; Jianqing Fan; Han Liu (CoRR, 2025)

A Novel P-bit-based Probabilistic Computing Approach for Solving the 3-D Protein Folding Problem. - Chao Fang; Yihan He; Xiao Gong; Gengchiau Liang (CoRR, 2025)