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2020 – today
- 2024
[c20]Chuanhao Sun, Zhihang Yuan, Kai Xu, Luo Mai, N. Siddharth, Shuo Chen, Mahesh K. Marina:
Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding. ICML 2024- [c19]Yao Fu, Leyang Xue, Yeqi Huang, Andrei-Octavian Brabete, Dmitrii Ustiugov, Yuvraj Patel, Luo Mai:
ServerlessLLM: Low-Latency Serverless Inference for Large Language Models. OSDI 2024: 135-153 - [c18]Marcel Wagenländer
, Guo Li, Bo Zhao, Luo Mai, Peter R. Pietzuch:
Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections. SOSP 2024: 195-210 - [i16]Yao Fu, Leyang Xue, Yeqi Huang, Andrei-Octavian Brabete, Dmitrii Ustiugov, Yuvraj Patel, Luo Mai:
ServerlessLLM: Locality-Enhanced Serverless Inference for Large Language Models. CoRR abs/2401.14351 (2024) - [i15]Leyang Xue, Yao Fu, Zhan Lu, Luo Mai, Mahesh K. Marina:
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving. CoRR abs/2401.14361 (2024) - [i14]Chuanhao Sun, Zhihang Yuan, Kai Xu, Luo Mai, N. Siddharth, Shuo Chen, Mahesh K. Marina:
Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding. CoRR abs/2407.09370 (2024) - [i13]Chuanhao Sun, Thanos Triantafyllou, Anthos Makris, Maja Drmac, Kai Xu, Luo Mai, Mahesh K. Marina:
PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis. CoRR abs/2410.05468 (2024) - 2023
- [j6]Muning Wen, Runji Lin, Hanjing Wang, Yaodong Yang, Ying Wen, Luo Mai, Jun Wang, Hai-Feng Zhang, Weinan Zhang:
Large sequence models for sequential decision-making: a survey. Frontiers Comput. Sci. 17(6): 176349 (2023) - [j5]Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang:
TorchOpt: An Efficient Library for Differentiable Optimization. J. Mach. Learn. Res. 24: 367:1-367:14 (2023) - [c17]Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai:
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. ICML 2023: 36380-36390 - [i12]Zeyuan Tan, Xiulong Yuan, Congjie He, Man-Kit Sit, Guo Li, Xiaoze Liu, Baole Ai, Kai Zeng, Peter R. Pietzuch, Luo Mai:
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness. CoRR abs/2305.10863 (2023) - [i11]Muning Wen, Runji Lin, Hanjing Wang, Yaodong Yang, Ying Wen, Luo Mai, Jun Wang, Haifeng Zhang, Weinan Zhang:
Large Sequence Models for Sequential Decision-Making: A Survey. CoRR abs/2306.13945 (2023) - [i10]Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai:
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. CoRR abs/2310.05205 (2023) - [i9]Marcel Wagenländer, Guo Li, Bo Zhao, Luo Mai, Peter R. Pietzuch:
TENPLEX: Changing Resources of Deep Learning Jobs using Parallelizable Tensor Collections. CoRR abs/2312.05181 (2023) - 2022
- [c16]Jie Ren, Wenteng Liang, Ran Yan, Luo Mai, Shiwen Liu, Xiao Liu:
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment. ECCV (37) 2022: 715-731 - [c15]Bo Liu, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang, Jun Wang, Yaodong Yang:
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. NeurIPS 2022 - [c14]Chijun Sima, Yao Fu, Man-Kit Sit, Liyi Guo, Xuri Gong, Feng Lin, Junyu Wu, Yongsheng Li, Haidong Rong, Pierre-Louis Aublin, Luo Mai:
Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update. OSDI 2022: 821-839 - [i8]Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang:
TorchOpt: An Efficient Library for Differentiable Optimization. CoRR abs/2211.06934 (2022) - 2021
- [c13]Zihan Ding, Tianyang Yu, Hongming Zhang, Yanhua Huang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong:
Efficient Reinforcement Learning Development with RLzoo. ACM Multimedia 2021: 3759-3762 - [c12]Yixiao Guo, Jiawei Liu, Guo Li, Luo Mai, Hao Dong:
Fast and Flexible Human Pose Estimation with HyperPose. ACM Multimedia 2021: 3763-3766 - [c11]Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju:
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. NSDI 2021: 389-405 - [i7]Yixiao Guo, Jiawei Liu, Guo Li, Luo Mai, Hao Dong:
Fast and Flexible Human Pose Estimation with HyperPose. CoRR abs/2108.11826 (2021) - [i6]Jie Ren, Wenteng Liang, Ran Yan, Luo Mai, Shiwen Liu, Xiao Liu:
MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment. CoRR abs/2112.01349 (2021) - 2020
- [c10]Marcel Wagenländer, Luo Mai, Guo Li, Peter R. Pietzuch:
Spotnik: Designing Distributed Machine Learning for Transient Cloud Resources. HotCloud 2020 - [c9]Luo Mai, Guo Li, Marcel Wagenländer, Konstantinos Fertakis, Andrei-Octavian Brabete, Peter R. Pietzuch:
KungFu: Making Training in Distributed Machine Learning Adaptive. OSDI 2020: 937-954 - [i5]Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Luo Mai, Hao Dong:
RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library. CoRR abs/2009.08644 (2020) - [i4]Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju:
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. CoRR abs/2010.03035 (2020)
2010 – 2019
- 2019
- [j4]Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, Peter R. Pietzuch:
Crossbow: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers. Proc. VLDB Endow. 12(11): 1399-1413 (2019) - [j3]Luo Mai, Alexandros Koliousis, Guo Li, Andrei-Octavian Brabete, Peter R. Pietzuch:
Taming Hyper-parameters in Deep Learning Systems. ACM SIGOPS Oper. Syst. Rev. 53(1): 52-58 (2019) - [i3]Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, Peter R. Pietzuch:
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers. CoRR abs/1901.02244 (2019) - 2018
- [j2]Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanam Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao:
Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. Proc. VLDB Endow. 11(10): 1303-1316 (2018) - 2017
- [b1]Luo Mai:
Towards efficient big data processing in data centres. Imperial College London, UK, 2017 - [c8]Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo:
TensorLayer: A Versatile Library for Efficient Deep Learning Development. ACM Multimedia 2017: 1201-1204 - [c7]Nik Sultana, Salvator Galea, David Greaves, Marcin Wójcik, Jonny Shipton, Richard G. Clegg, Luo Mai, Pietro Bressana, Robert Soulé, Richard Mortier, Paolo Costa, Peter R. Pietzuch, Jon Crowcroft, Andrew W. Moore, Noa Zilberman:
Emu: Rapid Prototyping of Networking Services. USENIX ATC 2017: 459-471 - [i2]Nik Sultana, Salvator Galea, David Greaves, Marcin Wójcik, Noa Zilberman, Richard G. Clegg, Luo Mai, Richard Mortier, Peter R. Pietzuch, Jon Crowcroft, Andrew W. Moore:
Extending programs with debug-related features, with application to hardware development. CoRR abs/1705.09902 (2017) - [i1]Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo:
TensorLayer: A Versatile Library for Efficient Deep Learning Development. CoRR abs/1707.08551 (2017) - 2016
- [c6]Da Yu, Luo Mai, Somaya Arianfar, Rodrigo Fonseca, Orran Krieger, David Oran:
Towards a Network Marketplace in a Cloud. HotCloud 2016 - [c5]Abdul Alim, Richard G. Clegg, Luo Mai, Lukas Rupprecht, Eric Seckler, Paolo Costa, Peter R. Pietzuch, Alexander L. Wolf, Nik Sultana, Jon Crowcroft, Anil Madhavapeddy, Andrew W. Moore, Richard Mortier, Masoud Koleini, Luis Oviedo, Matteo Migliavacca, Derek McAuley:
FLICK: Developing and Running Application-Specific Network Services. USENIX ATC 2016: 1-14 - 2015
- [c4]Luo Mai, Chuntao Hong, Paolo Costa:
Optimizing Network Performance in Distributed Machine Learning. HotCloud 2015 - 2014
- [c3]Luo Mai, Lukas Rupprecht, Abdul Alim, Paolo Costa, Matteo Migliavacca, Peter R. Pietzuch, Alexander L. Wolf:
NetAgg: Using Middleboxes for Application-specific On-path Aggregation in Data Centres. CoNEXT 2014: 249-262 - 2013
- [c2]Luo Mai, Lukas Rupprecht, Paolo Costa, Matteo Migliavacca, Peter R. Pietzuch, Alexander L. Wolf:
Supporting application-specific in-network processing in data centres. SIGCOMM 2013: 519-520 - 2012
- [j1]Junzhao Du, Hui Liu, Longfei Shangguan, Luo Mai, Kai Wang, Shucong Li:
Rendezvous Data Collection Using a Mobile Element in Heterogeneous Sensor Networks. Int. J. Distributed Sens. Networks 8 (2012) - 2011
- [c1]Luo Mai, Longfei Shangguan, Chao Lang, Junzhao Du, Hui Liu, Zhenjiang Li, Mo Li:
Load Balanced Rendezvous Data Collection in Wireless Sensor Networks. MASS 2011: 282-291
Coauthor Index
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last updated on 2024-12-02 22:28 CET by the dblp team
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