Linjian Ma
Linjian Ma
Research scientist, Meta Platforms, Inc.
Verified email at - Homepage
Cited by
Cited by
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
Fast and accurate randomized algorithms for low-rank tensor decompositions
L Ma, E Solomonik
Advances in Neural Information Processing Systems 34, 2021
Inefficiency of k-fac for large batch size training
L Ma, G Montague, J Ye, Z Yao, A Gholami, K Keutzer, M Mahoney
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5053-5060, 2020
Accelerating alternating least squares for tensor decomposition by pairwise perturbation
L Ma, E Solomonik
Numerical Linear Algebra with Applications, e2431, 2022
Comparison of Accuracy and Scalability of Gauss--Newton and Alternating Least Squares for CANDECOMC/PARAFAC Decomposition
N Singh, L Ma, H Yang, E Solomonik
SIAM Journal on Scientific Computing 43 (4), C290-C311, 2021
A multiscale model for electrochemical reactions in LSCF based solid oxide cells
L Ma, P Priya, NR Aluru
Journal of The Electrochemical Society 165 (14), F1232, 2018
MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models
Z Yao, L Ma, S Shen, K Keutzer, MW Mahoney
arXiv preprint arXiv:2105.14636, 2021
Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree
L Ma, E Solomonik
2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2021
AutoHOOT: Automatic high-order optimization for tensors
L Ma, J Ye, E Solomonik
Proceedings of the ACM International Conference on Parallel Architectures …, 2020
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs
L Ma, E Solomonik
arXiv preprint arXiv:2205.13163, 2022
Low rank approximation in simulations of quantum algorithms
L Ma, C Yang
Journal of Computational Science, 101561, 2022
Tensor Rank and Other Multipartite Entanglement Measures of Graph States
L Schatzki, L Ma, E Solomonik, E Chitambar
arXiv preprint arXiv:2209.06320, 2022
LEAP: Learnable Pruning for Transformer-based Models
Z Yao, X Wu, L Ma, S Shen, K Keutzer, MW Mahoney, Y He
arXiv e-prints, arXiv: 2105.14636, 2021
Approximate Contraction of Arbitrary Tensor Networks with a Flexible and Efficient Density Matrix Algorithm
L Ma, M Fishman, M Stoudenmire, E Solomonik
arXiv preprint arXiv:2406.09769, 2024
Leveraging the Human Ventral Visual Stream to Improve Neural Network Robustness
Z Shao, L Ma, B Li, DM Beck
arXiv preprint arXiv:2405.02564, 2024
Towards efficient algorithms and systems for tensor decompositions and tensor networks
L Ma
University of Illinois at Urbana-Champaign, 2023
Schmidt and Other Multipartite Entanglement Measures of Graph States
L Schatzki, L Ma, Y Pang, E Chitambar, E Solomonik
Bulletin of the American Physical Society, 2022
A multiscale model for the oxide ion conducting and proton conducting solid oxide cells
L Ma
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