| Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network J Diffenderfer, B Kailkhura arXiv preprint arXiv:2103.09377, 2021 | 107 | 2021 |
| Error analysis of zfp compression for floating-point data J Diffenderfer, AL Fox, JA Hittinger, G Sanders, PG Lindstrom SIAM Journal on Scientific Computing 41 (3), A1867-A1898, 2019 | 88 | 2019 |
| A winning hand: Compressing deep networks can improve out-of-distribution robustness J Diffenderfer, B Bartoldson, S Chaganti, J Zhang, B Kailkhura Advances in neural information processing systems 34, 664-676, 2021 | 79 | 2021 |
| Deepzero: Scaling up zeroth-order optimization for deep model training A Chen, Y Zhang, J Jia, J Diffenderfer, J Liu, K Parasyris, Y Zhang, ... arXiv preprint arXiv:2310.02025, 2023 | 35 | 2023 |
| Gtbench: Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations J Duan, R Zhang, J Diffenderfer, B Kailkhura, L Sun, E Stengel-Eskin, ... arXiv preprint arXiv:2402.12348, 2024 | 33 | 2024 |
| Soul: Unlocking the power of second-order optimization for llm unlearning J Jia, Y Zhang, Y Zhang, J Liu, B Runwal, J Diffenderfer, B Kailkhura, ... arXiv preprint arXiv:2404.18239, 2024 | 24 | 2024 |
| Stability analysis of inline ZFP compression for floating-point data in iterative methods A Fox, J Diffenderfer, J Hittinger, G Sanders, P Lindstrom SIAM Journal on Scientific Computing 42 (5), A2701-A2730, 2020 | 24 | 2020 |
| Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies BR Bartoldson, J Diffenderfer, K Parasyris, B Kailkhura arXiv preprint arXiv:2404.09349, 2024 | 17 | 2024 |
| HPAC: evaluating approximate computing techniques on HPC OpenMP applications K Parasyris, G Georgakoudis, H Menon, J Diffenderfer, I Laguna, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 16 | 2021 |
| Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression J Hong, J Duan, C Zhang, Z Li, C Xie, K Lieberman, J Diffenderfer, ... arXiv preprint arXiv:2403.15447, 2024 | 14 | 2024 |
| Algorithm 1035: a gradient-based implementation of the polyhedral active set algorithm WW Hager, H Zhang ACM Transactions on Mathematical Software 49 (2), 1-13, 2023 | 8 | 2023 |
| Approximate computing through the lens of uncertainty quantification K Parasyris, J Diffenderfer, H Menon, I Laguna, J Vanover, R Vogt, ... SC22: International Conference for High Performance Computing, Networking …, 2022 | 7 | 2022 |
| Benchmarking test-time unsupervised deep neural network adaptation on edge devices K Bhardwaj, J Diffenderfer, B Kailkhura, M Gokhale 2022 IEEE International Symposium on Performance Analysis of Systems and …, 2022 | 7 | 2022 |
| ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models J Duan, S Wang, J Diffenderfer, L Sun, T Chen, B Kailkhura, K Xu Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 6 | 2024 |
| A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks S Liu, B Bocklund, J Diffenderfer, S Chaganti, B Kailkhura, SK McCall, ... npj Computational Materials 10 (1), 172, 2024 | 5 | 2024 |
| Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations J Duan, R Zhang, J Diffenderfer, B Kailkhura, L Sun, E Stengel-Eskin, ... arXiv preprint arXiv:2402.12348, 2024 | 5 | 2024 |
| Unsupervised test-time adaptation of deep neural networks at the edge: a case study K Bhardwaj, J Diffenderfer, B Kailkhura, M Gokhale 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 412-417, 2022 | 5 | 2022 |
| QDOT: Quantized dot product kernel for approximate high-performance computing J Diffenderfer, D Osei-Kuffuor, H Menon arXiv preprint arXiv:2105.00115, 2021 | 5 | 2021 |
| Variable precision computing JAF Hittinger, PG Lindstrom, H Bhatia, PT Bremer, DM Copeland, ... Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2019 | 5 | 2019 |
| Zeroth-order sciml: Non-intrusive integration of scientific software with deep learning I Tsaknakis, B Kailkhura, S Liu, D Loveland, J Diffenderfer, AM Hiszpanski, ... arXiv preprint arXiv:2206.02785, 2022 | 4 | 2022 |
Bhavya KailkhuraResearch Scientist, Lawrence Livermore National LaboratoryZweryfikowany adres z llnl.gov
