Jiyan Yang
Jiyan Yang
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Zitiert von
Zitiert von
A study of BFLOAT16 for deep learning training
D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ...
arXiv preprint arXiv:1905.12322, 2019
Quasi-Monte Carlo feature maps for shift-invariant kernels
J Yang, V Sindhwani, H Avron, MW Mahoney
International Conference on Machine Learning (ICML 2014), 2014
Software-hardware co-design for fast and scalable training of deep learning recommendation models
D Mudigere, Y Hao, J Huang, Z Jia, A Tulloch, S Sridharan, X Liu, ...
Proceedings of the 49th Annual International Symposium on Computer …, 2022
Sub-sampled Newton methods with non-uniform sampling
P Xu, J Yang, F Roosta, C Ré, MW Mahoney
Advances in Neural Information Processing Systems 29, 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
Mixed dimension embeddings with application to memory-efficient recommendation systems
AA Ginart, M Naumov, D Mudigere, J Yang, J Zou
2021 IEEE International Symposium on Information Theory (ISIT), 2786-2791, 2021
Towards automated neural interaction discovery for click-through rate prediction
Q Song, D Cheng, H Zhou, J Yang, Y Tian, X Hu
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
Deep learning training in facebook data centers: Design of scale-up and scale-out systems
M Naumov, J Kim, D Mudigere, S Sridharan, X Wang, W Zhao, S Yilmaz, ...
arXiv preprint arXiv:2003.09518, 2020
Matrix factorizations at scale: A comparison of scientific data analytics in Spark and C+ MPI using three case studies
A Gittens, A Devarakonda, E Racah, M Ringenburg, L Gerhardt, ...
2016 IEEE International Conference on Big Data (Big Data), 204-213, 2016
Implementing randomized matrix algorithms in parallel and distributed environments
J Yang, X Meng, MW Mahoney
Proceedings of the IEEE 104 (1), 58-92, 2015
Online modified greedy algorithm for storage control under uncertainty
J Qin, Y Chow, J Yang, R Rajagopal
IEEE Transactions on Power Systems 31 (3), 1729-1743, 2015
Random laplace feature maps for semigroup kernels on histograms
J Yang, V Sindhwani, Q Fan, H Avron, MW Mahoney
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014
Quantile regression for large-scale applications
J Yang, X Meng, M Mahoney
International Conference on Machine Learning, 881-887, 2013
Weighted SGD for Regression with Randomized Preconditioning
J Yang, YL Chow, C Ré, MW Mahoney
Journal of Machine Learning Research 18 (211), 1-43, 2018
Distributed online modified greedy algorithm for networked storage operation under uncertainty
J Qin, Y Chow, J Yang, R Rajagopal
IEEE Transactions on Smart Grid 7 (2), 1106-1118, 2015
Identifying important ions and positions in mass spectrometry imaging data using CUR matrix decompositions
J Yang, O Rubel, Prabhat, MW Mahoney, BP Bowen
Analytical chemistry 87 (9), 4658-4666, 2015
Post-training 4-bit quantization on embedding tables
H Guan, A Malevich, J Yang, J Park, H Yuen
arXiv preprint arXiv:1911.02079, 2019
Modeling and online control of generalized energy storage networks
J Qin, Y Chow, J Yang, R Rajagopal
Proceedings of the 5th international conference on Future energy systems, 27-38, 2014
Understanding and improving failure tolerant training for deep learning recommendation with partial recovery
K Maeng, S Bharuka, I Gao, M Jeffrey, V Saraph, BY Su, C Trippel, J Yang, ...
Proceedings of Machine Learning and Systems 3, 637-651, 2021
Training with low-precision embedding tables
J Zhang, J Yang, H Yuen
Systems for Machine Learning Workshop at NeurIPS 2018, 2018
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