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2020 – today
- 2024
[j2]Peichao Cong
, Yutao Xu, Tianheng Li, Shanda Li, Hao Feng, Xin Zhang:
KM-Mask RCNN: A Lightweight Instance Segmentation Algorithm for Strawberries With Multiple Growth Cycles. IEEE Access 12: 169291-169309 (2024)- [j1]Peichao Cong, Hao Feng, Shanda Li, Tianheng Li, Yutao Xu, Xin Zhang:
A visual detection algorithm for autonomous driving road environment perception. Eng. Appl. Artif. Intell. 133: 108034 (2024) - [c6]Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller:
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. AISTATS 2024: 2278-2286 - [c5]Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli:
Functional Interpolation for Relative Positions improves Long Context Transformers. ICLR 2024 - [i8]Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang:
An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models. CoRR abs/2408.00724 (2024) - 2023
- [c4]Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. AISTATS 2023: 3034-3047 - [i7]Krzysztof Marcin Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller:
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. CoRR abs/2302.01925 (2023) - [i6]Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli:
Functional Interpolation for Relative Positions Improves Long Context Transformers. CoRR abs/2310.04418 (2023) - 2022
- [c3]Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
Your Transformer May Not be as Powerful as You Expect. NeurIPS 2022 - [c2]Chuwei Wang, Shanda Li, Di He, Liwei Wang:
Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? NeurIPS 2022 - [i5]Di He, Wenlei Shi, Shanda Li, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. CoRR abs/2202.09340 (2022) - [i4]Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
Your Transformer May Not be as Powerful as You Expect. CoRR abs/2205.13401 (2022) - [i3]Chuwei Wang, Shanda Li, Di He, Liwei Wang:
Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? CoRR abs/2206.02016 (2022) - 2021
- [c1]Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu:
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding. NeurIPS 2021: 22795-22807 - [i2]Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu:
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding. CoRR abs/2106.12566 (2021) - [i1]Shanda Li, Xiangning Chen, Di He, Cho-Jui Hsieh:
Can Vision Transformers Perform Convolution? CoRR abs/2111.01353 (2021)
Coauthor Index
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