Li, Shanda

10 publications

ICLR 2025 Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang

ICML 2025 Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators Shanda Li, Shinjae Yoo, Yiming Yang

ICLR 2025 TFG-Flow: Training-Free Guidance in Multimodal Generative Flow Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma

ICLR 2024 Functional Interpolation for Relative Positions Improves Long Context Transformers Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli

NeurIPSW 2024 Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang

AISTATS 2024 Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamas Sarlos, Thomas Weingarten, Adrian Weller

AISTATS 2023 Learning Physics-Informed Neural Networks Without Stacked Back-Propagation Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu

NeurIPS 2022 Is $l^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? Chuwei Wang, Shanda Li, Di He, Liwei Wang

NeurIPS 2022 Your Transformer May Not Be as Powerful as You Expect Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He

NeurIPS 2021 Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu