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2020 – today
- 2024
[c12]Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris, Jiancheng Liu, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu:
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training. ICLR 2024- [c11]Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura:
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. ICML 2024 - [c10]Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Kumar Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li:
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. ICML 2024 - [c9]Jinhao Duan, Shiqi Wang, James Diffenderfer, Lichao Sun, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu:
ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models. NAACL-HLT 2024: 2232-2246 - [i17]Jinhao Duan, Renming Zhang, James Diffenderfer
, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu:
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations. CoRR abs/2402.12348 (2024) - [i16]Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li:
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. CoRR abs/2403.15447 (2024) - [i15]Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura:
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. CoRR abs/2404.09349 (2024) - [i14]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. CoRR abs/2404.11766 (2024) - [i13]Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu:
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. CoRR abs/2404.18239 (2024) - 2023
- [j6]Harshitha Menon, James Diffenderfer, Giorgis Georgakoudis, Ignacio Laguna, Michael O. Lam, Daniel Osei-Kuffuor, Konstantinos Parasyris, Jackson Vanover:
Approximate High-Performance Computing: A Fast and Energy-Efficient Computing Paradigm in the Post-Moore Era. IT Prof. 25(2): 7-15 (2023) - [c8]Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura:
Neural Image Compression: Generalization, Robustness, and Spectral Biases. NeurIPS 2023 - [i12]Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura:
Neural Image Compression: Generalization, Robustness, and Spectral Biases. CoRR abs/2307.08657 (2023) - [i11]Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu:
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training. CoRR abs/2310.02025 (2023) - [i10]Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter A. Beerel:
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks. CoRR abs/2312.06900 (2023) - 2022
- [j5]James Diffenderfer, Daniel Osei-Kuffuor, Harshitha Menon:
A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products. SIAM J. Sci. Comput. 44(3): 1290- (2022) - [c7]Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale:
Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study. DATE 2022: 412-417 - [c6]Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale:
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. ISPASS 2022: 236-238 - [c5]Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer:
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. NeurIPS 2022 - [c4]Konstantinos Parasyris, James Diffenderfer, Harshitha Menon, Ignacio Laguna, Jackson Vanover, Ryan Vogt, Daniel Osei-Kuffuor:
Approximate Computing Through the Lens of Uncertainty Quantification. SC 2022: 67:1-67:14 - [i9]Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale:
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. CoRR abs/2203.11295 (2022) - [i8]Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong:
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. CoRR abs/2206.02785 (2022) - [i7]Zhimin Li, Shusen Liu, Xin Yu, Bhavya Kailkhura, Jie Cao, James Daniel Diffenderfer, Peer-Timo Bremer, Valerio Pascucci:
"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches. CoRR abs/2206.07918 (2022) - [i6]Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Peer-Timo Bremer:
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. CoRR abs/2207.04075 (2022) - [i5]Hao Cheng, Pu Zhao, Yize Li, Xue Lin, James Diffenderfer, Ryan A. Goldhahn, Bhavya Kailkhura:
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed. CoRR abs/2209.12839 (2022) - 2021
- [c3]James Diffenderfer, Bhavya Kailkhura:
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. ICLR 2021 - [c2]James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura:
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. NeurIPS 2021: 664-676 - [c1]Konstantinos Parasyris, Giorgis Georgakoudis, Harshitha Menon, James Diffenderfer, Ignacio Laguna, Daniel Osei-Kuffuor, Markus Schordan:
HPAC: evaluating approximate computing techniques on HPC OpenMP applications. SC 2021: 86 - [i4]James Diffenderfer, Bhavya Kailkhura:
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. CoRR abs/2103.09377 (2021) - [i3]James Diffenderfer, Daniel Osei-Kuffuor, Harshitha Menon:
QDOT: Quantized Dot Product Kernel for Approximate High-Performance Computing. CoRR abs/2105.00115 (2021) - [i2]James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura:
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. CoRR abs/2106.09129 (2021) - 2020
- [j4]Alyson Fox, James Diffenderfer, Jeffrey Hittinger, Geoffrey Sanders, Peter Lindstrom:
Stability Analysis of Inline ZFP Compression for Floating-Point Data in Iterative Methods. SIAM J. Sci. Comput. 42(5): A2701-A2730 (2020) - [i1]Alyson Fox, James Diffenderfer, Jeffrey Hittinger, Geoffrey Sanders, Peter Lindstrom:
Stability Analysis of Inline ZFP Compression for Floating-Point Data in Iterative Methods. CoRR abs/2003.02324 (2020)
2010 – 2019
- 2019
- [j3]James Diffenderfer, Alyson Fox, Jeffrey A. F. Hittinger, Geoffrey Sanders, Peter G. Lindstrom:
Error Analysis of ZFP Compression for Floating-Point Data. SIAM J. Sci. Comput. 41(3): A1867-A1898 (2019) - 2017
- [j2]Xiezhang Li, James Diffenderfer, Jiehua Zhu:
Construction of a full row-rank matrix system for multiple scanning directions in discrete tomography. J. Comput. Appl. Math. 311: 529-538 (2017) - 2014
- [j1]James Daniel Diffenderfer:
Building a Better Bijection Between Classes of Compositions. Integers 14: A44 (2014)
Coauthor Index
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last updated on 2024-10-07 21:24 CEST by the dblp team
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