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2020 – today
- 2024
[c9]Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa:
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. ICML 2024- [c8]Jerry Chee
, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis:
Harm Mitigation in Recommender Systems under User Preference Dynamics. KDD 2024: 255-265 - [i8]Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa:
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. CoRR abs/2402.04396 (2024) - [i7]Jerry Chee, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis:
Harm Mitigation in Recommender Systems under User Preference Dynamics. CoRR abs/2406.09882 (2024) - 2023
- [c7]Jerry Chee, Hwanwoo Kim, Panos Toulis:
"Plus/minus the learning rate": Easy and Scalable Statistical Inference with SGD. AISTATS 2023: 2285-2309 - [c6]Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. NeurIPS 2023 - [i6]Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. CoRR abs/2307.13304 (2023) - 2022
- [c5]Chengrun Yang, Ziyang Wu, Jerry Chee, Christopher De Sa, Madeleine Udell:
How Low Can We Go: Trading Memory for Error in Low-Precision Training. ICLR 2022 - [c4]Jerry Chee, Sebastian Braun, Vishak Gopal, Ross Cutler:
Performance optimizations on U-Net speech enhancement models. MMSP 2022: 1-6 - [c3]Jerry Chee, Megan Flynn, Anil Damle, Christopher De Sa:
Model Preserving Compression for Neural Networks. NeurIPS 2022 - 2021
- [i5]Chengrun Yang, Ziyang Wu, Jerry Chee, Christopher De Sa, Madeleine Udell:
How Low Can We Go: Trading Memory for Error in Low-Precision Training. CoRR abs/2106.09686 (2021) - [i4]Jerry Chee, Megan Renz, Anil Damle, Chris De Sa:
Pruning Neural Networks with Interpolative Decompositions. CoRR abs/2108.00065 (2021) - [i3]Jerry Chee, Sebastian Braun, Vishak Gopal, Ross Cutler:
Performance optimizations on deep noise suppression models. CoRR abs/2110.04378 (2021) - 2020
- [c2]Jerry Chee, Ping Li:
Understanding and Detecting Convergence for Stochastic Gradient Descent with Momentum. IEEE BigData 2020: 133-140 - [i2]Jerry Chee, Ping Li:
Understanding and Detecting Convergence for Stochastic Gradient Descent with Momentum. CoRR abs/2008.12224 (2020)
2010 – 2019
- 2018
- [c1]Jerry Chee, Panos Toulis:
Convergence diagnostics for stochastic gradient descent with constant learning rate. AISTATS 2018: 1476-1485 - 2017
- [i1]Jerry Chee, Panos Toulis:
Convergence diagnostics for stochastic gradient descent with constant step size. CoRR abs/1710.06382 (2017)
Coauthor Index
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