Conglong Li

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He YuxiongMicrosoft ResearchDirección de correo verificada de microsoft.com
Minjia ZhangUniversity of Illinois at Urbana-ChampaginDirección de correo verificada de illinois.edu
Zhewei YaoSnowflakeDirección de correo verificada de snowflake.com
David G. AndersenProfessor of Computer Science, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Xiaoxia (Shirley) Wu 吴晓霞DeepSpeed Team @ MicrosoftDirección de correo verificada de microsoft.com
Ammar Ahmad AwanMicrosoftDirección de correo verificada de osu.edu
Reza Yazdani AminabadiMicrosoft ResearchDirección de correo verificada de microsoft.com
Michael KaminskyBrdgAI & Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Hyeontaek LimGoogle DeepMindDirección de correo verificada de google.com
Giulio ZhouGraduate Student, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Christopher CanelPhD student, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Jeff RasleyMicrosoftDirección de correo verificada de microsoft.com
Connor HolmesComputer Science PhD Candidate, Colorado School of MinesDirección de correo verificada de mymail.mines.edu
Srinivasan SeshanCarnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Alex C. SnoerenUC San DiegoDirección de correo verificada de cs.ucsd.edu
Matthew K. MukerjeeSoftware Engineer, Nefeli NetworksDirección de correo verificada de cs.cmu.edu
George PorterProfessor of Computer Science and Engineering, UC San DiegoDirección de correo verificada de ucsd.edu
Hanlin TangUniversity of RochesterDirección de correo verificada de ur.rochester.edu
Alan CoxRice UniversityDirección de correo verificada de cs.rice.edu
He LiuUniversity of California, San DiegoDirección de correo verificada de cs.ucsd.edu

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Conglong Li

Senior Researcher at Microsoft, CMU Ph.D.

Dirección de correo verificada de microsoft.com - Página principal

Natural Language Processing Deep Learning Machine Learning Artificial Intelligence Distributed Systems


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ...	1162	2023
Zeroquant: Efficient and affordable post-training quantization for large-scale transformers Z Yao, R Yazdani Aminabadi, M Zhang, X Wu, C Li, Y He Advances in Neural Information Processing Systems 35, 27168-27183, 2022	184	2022
Scaling Video Analytics on Constrained Edge Nodes C Canel, T Kim, G Zhou, C Li, H Lim, DG Andersen, M Kaminsky, ... MLSys Conference 2019, 2019	156	2019
Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale S Rajbhandari, C Li, Z Yao, M Zhang, RY Aminabadi, AA Awan, J Rasley, ... International conference on machine learning, 18332-18346, 2022	143	2022
Scheduling Techniques for Hybrid Circuit/Packet Networks H Liu, MK Mukerjee, C Li, N Feltman, G Papen, S Savage, S Seshan, ... 11th International Conference on emerging Networking EXperiments and …, 2015	139	2015
1-bit adam: Communication efficient large-scale training with adam’s convergence speed H Tang, S Gan, AA Awan, S Rajbhandari, C Li, X Lian, J Liu, C Zhang, ... International Conference on Machine Learning, 10118-10129, 2021	70	2021
GD-Wheel: a cost-aware replacement policy for key-value stores C Li, AL Cox Proceedings of the Tenth European Conference on Computer Systems, 5, 2015	70	2015
Improving approximate nearest neighbor search through learned adaptive early termination C Li, M Zhang, DG Andersen, Y He Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020	44	2020
The stability-efficiency dilemma: Investigating sequence length warmup for training GPT models C Li, M Zhang, Y He Advances in Neural Information Processing Systems 35, 26736-26750, 2022	42*	2022
Deepspeed-chat: Easy, fast and affordable rlhf training of chatgpt-like models at all scales Z Yao, RY Aminabadi, O Ruwase, S Rajbhandari, X Wu, AA Awan, ... arXiv preprint arXiv:2308.01320, 2023	26	2023
1-bit LAMB: communication efficient large-scale large-batch training with LAMB’s convergence speed C Li, AA Awan, H Tang, S Rajbhandari, Y He 2022 IEEE 29th International Conference on High Performance Computing, Data …, 2022	26	2022
Maximizing communication efficiency for large-scale training via 0/1 adam Y Lu, C Li, M Zhang, C De Sa, Y He International Conference on Learning Representations, 2023	17	2023
Xtc: Extreme compression for pre-trained transformers made simple and efficient X Wu, Z Yao, M Zhang, C Li, Y He Advances in Neural Information Processing Systems 35, 3217-3231, 2022	17	2022
Using indirect routing to recover from network traffic scheduling estimation error C Li, MK Mukerjee, DG Andersen, S Seshan, M Kaminsky, G Porter, ... 2017 ACM/IEEE Symposium on Architectures for Networking and Communications …, 2017	14	2017
Better caching in search advertising systems with rapid refresh predictions C Li, DG Andersen, Q Fu, S Elnikety, Y He Proceedings of the 2018 World Wide Web Conference, 1875-1884, 2018	13	2018
Reducing DRAM row activations with eager read/write clustering M Jeon, C Li, AL Cox, S Rixner ACM Transactions on Architecture and Code Optimization (TACO) 10 (4), 1-25, 2013	12	2013
Random-ltd: Random and layerwise token dropping brings efficient training for large-scale transformers Z Yao, X Wu, C Li, C Holmes, M Zhang, C Li, Y He arXiv preprint arXiv:2211.11586, 2022	11	2022
Picking Interesting Frames in Streaming Video C Canel, T Kim, G Zhou, C Li, H Lim, DG Andersen, M Kaminsky, ... MLSys Conference 2018 (Poster), 2018	8	2018
Workload analysis and caching strategies for search advertising systems C Li, DG Andersen, Q Fu, S Elnikety, Y He Proceedings of the 2017 Symposium on Cloud Computing, 170-180, 2017	8	2017
Deepspeed data efficiency: Improving deep learning model quality and training efficiency via efficient data sampling and routing C Li, Z Yao, X Wu, M Zhang, C Holmes, C Li, Y He Proceedings of the AAAI Conference on Artificial Intelligence 38 (16), 18490 …, 2024	4	2024

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