| SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks L Wang, J Ye, Y Zhao, W Wu, A Li, SL Song, Z Xu, T Kraska Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of …, 2018 | 210 | 2018 |
| Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition J Ye, L Wang, G Li, D Chen, S Zhe, X Chu, Z Xu Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018 | 117 | 2018 |
| Alphax: Exploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search L Wang, Y Zhao, Y Jinnai, Y Tian, R Fonseca AAAI Conference on Artificial Intelligence (AAAI 2020), 2020 | 106* | 2020 |
| Accelerating Deep Neural Network Training With Inconsistent Stochastic Gradient Descent L Wang, Y Yang, R Min, S Chakradhar Neural Networks 93, 219-229, 2017 | 98 | 2017 |
| BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing L Wang, W Wu, Z Xu, J Xiao, Y Yang Proceedings of the 2016 International Conference on Supercomputing, 20, 2016 | 67 | 2016 |
| Few-shot Neural Architecture Search Y Zhao, L Wang, Y Tian, R Fonseca, T Guo ICML 2021, 2021 | 64 | 2021 |
| Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search L Wang, S Xie, T Li, R Fonseca, Y Tian IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 61* | 2021 |
| Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search L Wang, R Fonseca, Y Tian NeurIPS 2020, 2020 | 59 | 2020 |
| Warp-Consolidation: A Novel Execution Model for GPUs A Li, W Liu, L Wang, K Barker, SL Song Proceedings of the 2018 International Conference on Supercomputing, 2018 | 30 | 2018 |
| Adapt: An Event-based Adaptive Collective Communication Framework X Luo, W Wu, G Bosilca, T Patinyasakdikul, L Wang, J Dongarra Proceedings of the 27th International Symposium on High-Performance Parallel …, 2018 | 22 | 2018 |
| SuperNeurons: FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks L Wang, W Wu, J Zhang, H Liu, G Bosilca, M Herlihy, R Fonseca Proceedings of the 29th International Symposium on High-Performance Parallel …, 2020 | 17 | 2020 |
| Efficient Communications in Training Large Scale Neural Networks Y Zhao, L Wang, W Wu, G Bosilca, R Vuduc, J Ye, W Tang, Z Xu | 14 | 2017 |
| Large Scale Artificial Neural Network Training Using Multi-GPUs L Wang, W Wu, J Xiao, Y Yi Supercomputing 16, 2016 | 8 | 2016 |
| Multi-objective Optimization by Learning Space Partitions Y Zhao, L Wang, K Yang, T Zhang, T Guo, Y Tian ICLR 2022, 2022 | 7 | 2022 |
| GPUNet: Searching the Deployable Convolution Neural Networks for GPUs L Wang, C Yu, S Salian, S Kierat, S Migacz, AF Florea CVPR 2022, 2022 | 5 | 2022 |
| Learning Space Partitions for Path Planning K Yang, T Zhang, C Cummins, B Cui, B Steiner, L Wang, JE Gonzalez, ... NeurIPS 2021, 2021 | 4 | 2021 |
| BLASX L Wang, W Wu, Z Xu, J Xiao, Y Yang Proceedings of the 2016 International Conference on Supercomputing, 2016 | | 2016 |
Rodrigo FonsecaPrincipal Researcher, Microsoft Research在 microsoft.com 的电子邮件经过验证
