| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1263 | 2018 |
| A Survey of Quantization Methods for Efficient Neural Network Inference A Gholami, S Kim, Z Dong, Z Yao, MW Mahoney, K Keutzer arXiv preprint arXiv:2103.13630, 2021 | 334 | 2021 |
| Q-bert: Hessian based ultra low precision quantization of bert S Shen, Z Dong, J Ye, L Ma, Z Yao, A Gholami, MW Mahoney, K Keutzer Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8815-8821, 2020 | 333 | 2020 |
| Shift: A zero flop, zero parameter alternative to spatial convolutions B Wu, A Wan, X Yue, P Jin, S Zhao, N Golmant, A Gholaminejad, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 317 | 2018 |
| Hawq: Hessian aware quantization of neural networks with mixed-precision Z Dong, Z Yao, A Gholami, MW Mahoney, K Keutzer Proceedings of the IEEE/CVF International Conference on Computer Vision, 293-302, 2019 | 288 | 2019 |
| Squeezenext: Hardware-aware neural network design A Gholami, K Kwon, B Wu, Z Tai, X Yue, P Jin, S Zhao, K Keutzer Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 251 | 2018 |
| Zeroq: A novel zero shot quantization framework Y Cai, Z Yao, Z Dong, A Gholami, MW Mahoney, K Keutzer Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 240 | 2020 |
| Characterizing possible failure modes in physics-informed neural networks A Krishnapriyan, A Gholami, S Zhe, R Kirby, MW Mahoney Advances in Neural Information Processing Systems 34, 26548-26560, 2021 | 170 | 2021 |
| ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs A Gholami, K Keutzer, G Biros arXiv preprint arXiv:1902.10298, 2019 | 147 | 2019 |
| Pyhessian: Neural networks through the lens of the hessian Z Yao, A Gholami, K Keutzer, MW Mahoney 2020 IEEE International Conference on Big Data (Big Data), 581-590, 2020 | 144 | 2020 |
| Hessian-based analysis of large batch training and robustness to adversaries Z Yao, A Gholami, Q Lei, K Keutzer, MW Mahoney Advances in Neural Information Processing Systems 31, 4949-4959, 2018 | 132 | 2018 |
| Hawq-v2: Hessian aware trace-weighted quantization of neural networks Z Dong, Z Yao, D Arfeen, A Gholami, MW Mahoney, K Keutzer Advances in neural information processing systems 33, 18518-18529, 2020 | 128 | 2020 |
| Adahessian: An adaptive second order optimizer for machine learning Z Yao, A Gholami, S Shen, M Mustafa, K Keutzer, M Mahoney Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10665 …, 2021 | 117 | 2021 |
| I-bert: Integer-only bert quantization S Kim, A Gholami, Z Yao, MW Mahoney, K Keutzer International conference on machine learning, 5506-5518, 2021 | 99 | 2021 |
| Integrated model, batch, and domain parallelism in training neural networks A Gholami, A Azad, P Jin, K Keutzer, A Buluc Proceedings of the 30th on Symposium on Parallelism in Algorithms and …, 2018 | 96* | 2018 |
| FFT, FMM, or Multigrid? A comparative Study of State-Of-the-Art Poisson Solvers for Uniform and Nonuniform Grids in the Unit Cube A Gholami, D Malhotra, H Sundar, G Biros SIAM Journal on Scientific Computing 38 (3), C280-C306, 2016 | 95 | 2016 |
| Checkmate: Breaking the memory wall with optimal tensor rematerialization P Jain, A Jain, A Nrusimha, A Gholami, P Abbeel, J Gonzalez, K Keutzer, ... Proceedings of Machine Learning and Systems 2, 497-511, 2020 | 94 | 2020 |
| HAWQ-V3: Dyadic Neural Network Quantization Z Yao, Z Dong, Z Zheng, A Gholami, J Yu, E Tan, L Wang, Q Huang, ... International Conference on Machine Learning, 11875-11886, 2021 | 90 | 2021 |
| An inverse problem formulation for parameter estimation of a reaction–diffusion model of low grade gliomas A Gholami, A Mang, G Biros Journal of mathematical biology 72 (1), 409-433, 2016 | 73 | 2016 |
| On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent N Golmant, N Vemuri, Z Yao, V Feinberg, A Gholami, K Rothauge, ... arXiv preprint arXiv:1811.12941, 2018 | 71 | 2018 |
Kurt KeutzerProfessor of the Graduate School, EECS, University of California, BerkeleyVerified email at berkeley.edu
