Deep Learning Recommendation Model for Personalization and Recommendation Systems M Naumov, D Mudigere, HJM Shi, J Huang, N Sundaraman, J Park, ... arXiv preprint arXiv:1906.00091, 2019 | 714 | 2019 |
A progressive batching L-BFGS method for machine learning R Bollapragada, J Nocedal, D Mudigere, HJ Shi, PTP Tang International Conference on Machine Learning, 620-629, 2018 | 165 | 2018 |
A Primer on Coordinate Descent Algorithms HJM Shi, S Tu, Y Xu, W Yin arXiv preprint arXiv:1610.00040, 2016 | 117 | 2016 |
Compositional embeddings using complementary partitions for memory-efficient recommendation systems HJM Shi, D Mudigere, M Naumov, J Yang Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 111 | 2020 |
On the numerical performance of finite-difference-based methods for derivative-free optimization HJM Shi, M Qiming Xuan, F Oztoprak, J Nocedal Optimization Methods and Software 38 (2), 289-311, 2023 | 50* | 2023 |
Methods for Quantized Compressed Sensing HJM Shi, M Case, X Gu, S Tu, D Needell Information Theory and Applications, 2016 | 38 | 2016 |
Ground-Motion Prediction Equations for Arias Intensity Consistent with the NGA-West2 Ground-Motion Models C Abrahamson, HJM Shi, B Yang Pacific Earthquake Engineering Research Center, 2016 | 32 | 2016 |
A noise-tolerant quasi-Newton algorithm for unconstrained optimization HJM Shi, Y Xie, R Byrd, J Nocedal SIAM Journal on Optimization 32 (1), 29-55, 2022 | 29 | 2022 |
Adaptive finite-difference interval estimation for noisy derivative-free optimization HJM Shi, Y Xie, MQ Xuan, J Nocedal SIAM Journal on Scientific Computing 44 (4), A2302-A2321, 2022 | 19 | 2022 |
Optimizing Quantization for Lasso Recovery X Gu, S Tu, HJM Shi, M Case, D Needell, Y Plan arXiv preprint arXiv:1606.03055, 2016 | 6 | 2016 |
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale HJM Shi, TH Lee, S Iwasaki, J Gallego-Posada, Z Li, K Rangadurai, ... arXiv preprint arXiv:2309.06497, 2023 | 3 | 2023 |
PyTorch-LBFGS: A PyTorch implementation of L-BFGS HJM Shi, D Mudigere | 3 | 2018 |
Practical Algorithms for Learning Near-Isometric Linear Embeddings J Luo, K Shapiro, HJM Shi, Q Yang, K Zhu SIAM Undergraduate Research Online 9, 2016 | 3 | 2016 |
Methods for Stochastic, Noisy, and Derivative-Free Optimization HJM Shi Northwestern University, 2021 | | 2021 |
Additional Numerical Results for:“On the Numerical Performance of Finite-Difference Based Methods for Derivative-Free Optimization” HJM Shia, MQ Xuana, F Oztoprakb, J Nocedala | | |