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2020 – today
- 2024
[j14]Lijie Xu
, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems. VLDB J. 33(5): 1231-1255 (2024)- [c16]Lixi Zhou, Qi Lin, Kanchan Chowdhury, Saif Masood, Alexandre E. Eichenberger, Hong Min, Alexander Sim, Jie Wang, Yida Wang, Kesheng Wu, Binhang Yuan, Jia Zou:
Serving Deep Learning Models from Relational Databases. EDBT 2024: 717-724 - [c15]Youhe Jiang, Ran Yan, Xiaozhe Yao, Yang Zhou, Beidi Chen, Binhang Yuan:
HexGen: Generative Inference of Large Language Model over Heterogeneous Environment. ICML 2024 - [c14]Lin Lu, Chenxi Dai, Wangcheng Tao, Binhang Yuan, Yanan Sun, Pan Zhou:
Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training. ICML 2024 - [i30]Suyi Li, Hanfeng Lu, Tianyuan Wu, Minchen Yu, Qizhen Weng, Xusheng Chen, Yizhou Shan, Binhang Yuan, Wei Wang:
CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference. CoRR abs/2401.11240 (2024) - [i29]Changyue Liao, Mo Sun, Zihan Yang, Kaiqi Chen, Binhang Yuan, Fei Wu, Zeke Wang:
Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU. CoRR abs/2403.06504 (2024) - [i28]Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You, Binhang Yuan, Zeke Wang, Tao Lin:
DeFT: Flash Tree-attention with IO-Awareness for Efficient Tree-search-based LLM Inference. CoRR abs/2404.00242 (2024) - [i27]Xinrun Du, Zhouliang Yu, Songyang Gao, Ding Pan, Yuyang Cheng, Ziyang Ma, Ruibin Yuan, Xingwei Qu, Jiaheng Liu, Tianyu Zheng, Xinchen Luo, Guorui Zhou, Binhang Yuan, Wenhu Chen, Jie Fu, Ge Zhang:
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. CoRR abs/2404.04167 (2024) - [i26]Tianyi Bai, Hao Liang, Binwang Wan, Ling Yang, Bozhou Li, Yifan Wang, Bin Cui, Conghui He, Binhang Yuan, Wentao Zhang:
A Survey of Multimodal Large Language Model from A Data-centric Perspective. CoRR abs/2405.16640 (2024) - [i25]Lujia Zhang, Hanzhe Cui, Yurong Song, Chenyue Li, Binhang Yuan, Mengqian Lu:
On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study. CoRR abs/2407.17842 (2024) - [i24]Ran Yan, Youhe Jiang, Wangcheng Tao, Xiaonan Nie, Bin Cui, Binhang Yuan:
FlashFlex: Accommodating Large Language Model Training over Heterogeneous Environment. CoRR abs/2409.01143 (2024) - [i23]Yuxiang Huang, Binhang Yuan, Xu Han, Chaojun Xiao, Zhiyuan Liu:
Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads. CoRR abs/2410.01805 (2024) - [i22]Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen:
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild. CoRR abs/2410.05357 (2024) - [i21]Tianyi Bai, Ling Yang, Zhen Hao Wong, Jiahui Peng, Xinlin Zhuang, Chi Zhang, Lijun Wu, Jiantao Qiu, Wentao Zhang, Binhang Yuan, Conghui He:
Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining. CoRR abs/2410.08102 (2024) - 2023
- [j13]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [c13]Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. ICML 2023: 22137-22176 - [c12]Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang:
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU. ICML 2023: 31094-31116 - [c11]Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine:
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning. ICML 2023: 33581-33598 - [c10]Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang:
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. ICML 2023: 36058-36076 - [i20]Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark W. Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang:
High-throughput Generative Inference of Large Language Models with a Single GPU. CoRR abs/2303.06865 (2023) - [i19]Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine:
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning. CoRR abs/2306.00088 (2023) - [i18]Alexandre E. Eichenberger, Qi Lin, Saif Masood, Hong Min, Alexander Sim, Jie Wang, Yida Wang, Kesheng Wu, Binhang Yuan, Lixi Zhou, Jia Zou:
Serving Deep Learning Model in Relational Databases. CoRR abs/2310.04696 (2023) - [i17]Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. CoRR abs/2310.17157 (2023) - [i16]Youhe Jiang, Ran Yan, Xiaozhe Yao, Beidi Chen, Binhang Yuan:
HexGen: Generative Inference of Foundation Model over Heterogeneous Decentralized Environment. CoRR abs/2311.11514 (2023) - [i15]Lin Lu, Chenxi Dai, Wangcheng Tao, Binhang Yuan, Yanan Sun, Pan Zhou:
Exploring the Robustness of Decentralized Training for Large Language Models. CoRR abs/2312.00843 (2023) - 2022
- [j12]Zijian Ding, Guijin Wang, Huazhong Yang, Ping Zhang, Dapeng Fu, Zhen Yang, Xinkang Wang, Xia Wang, Zhourui Xia, Chiming Zhang, Wenjie Cai, Binhang Yuan, Dongya Jia, Bo Chen, Chengbin Huang, Jing Zhang, Yi Li, Shan Yang, Runnan He:
A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram. Medical Biol. Eng. Comput. 60(1): 33-45 (2022) - [j11]Binhang Yuan, Cameron R. Wolfe, Chen Dun, Yuxin Tang, Anastasios Kyrillidis, Chris Jermaine:
Distributed Learning of Fully Connected Neural Networks using Independent Subnet Training. Proc. VLDB Endow. 15(8): 1581-1590 (2022) - [j10]Huaqing Jin, Guosheng Yin, Binhang Yuan, Fei Jiang:
Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences. Technometrics 64(2): 177-186 (2022) - [c9]Rui Pan, Yiming Lei, Jialong Li, Zhiqiang Xie, Binhang Yuan, Yiting Xia:
Efficient flow scheduling in distributed deep learning training with echelon formation. HotNets 2022: 93-100 - [c8]Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu:
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters. KDD 2022: 3288-3298 - [c7]Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang:
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees. NeurIPS 2022 - [c6]Binhang Yuan, Yongjun He, Jared Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang:
Decentralized Training of Foundation Models in Heterogeneous Environments. NeurIPS 2022 - [c5]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle. SIGMOD Conference 2022: 1286-1300 - [i14]Binhang Yuan, Yongjun He, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang:
Decentralized Training of Foundation Models in Heterogeneous Environments. CoRR abs/2206.01288 (2022) - [i13]Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang:
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees. CoRR abs/2206.01299 (2022) - [i12]Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang:
Stochastic Gradient Descent without Full Data Shuffle. CoRR abs/2206.05830 (2022) - [i11]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. CoRR abs/2211.09110 (2022) - 2021
- [j9]Dimitrije Jankov, Binhang Yuan, Shangyu Luo, Chris Jermaine:
Distributed Numerical and Machine Learning Computations via Two-Phase Execution of Aggregated Join Trees. Proc. VLDB Endow. 14(7): 1228-1240 (2021) - [j8]Jia Zou, Amitabh Das, Pratik Barhate, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, Chris Jermaine:
Lachesis: Automated Partitioning for UDF-Centric Analytics. Proc. VLDB Endow. 14(8): 1262-1275 (2021) - [j7]Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, Chris Jermaine:
Tensor Relational Algebra for Distributed Machine Learning System Design. Proc. VLDB Endow. 14(8): 1338-1350 (2021) - [j6]Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang:
BAGUA: Scaling up Distributed Learning with System Relaxations. Proc. VLDB Endow. 15(4): 804-813 (2021) - [j5]Chengbin Huang, Weiting Chen, Mingsong Chen, Binhang Yuan:
A Feature Fusion Framework and Its Application to Automatic Seizure Detection. IEEE Signal Process. Lett. 28: 753-757 (2021) - [c4]Shangyu Luo, Dimitrije Jankov, Binhang Yuan, Chris Jermaine:
Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra. SIGMOD Conference 2021: 1222-1234 - [i10]Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang:
BAGUA: Scaling up Distributed Learning with System Relaxations. CoRR abs/2107.01499 (2021) - [i9]Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu:
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters. CoRR abs/2111.05897 (2021) - 2020
- [j4]Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao:
Declarative Recursive Computation on an RDBMS: or, Why You Should Use a Database For Distributed Machine Learning. SIGMOD Rec. 49(1): 43-50 (2020) - [i8]Binhang Yuan, Song Ge, Wenhui Xing:
A Federated Learning Framework for Healthcare IoT devices. CoRR abs/2005.05083 (2020) - [i7]Jia Zou, Pratik Barhate, Amitabh Das, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, Chris Jermaine:
Lachesis: Automated Generation of Persistent Partitionings for Big Data Applications. CoRR abs/2006.16529 (2020) - [i6]Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, Chris Jermaine:
Tensor Relational Algebra for Machine Learning System Design. CoRR abs/2009.00524 (2020)
2010 – 2019
- 2019
- [j3]Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao:
Declarative Recursive Computation on an RDBMS. Proc. VLDB Endow. 12(7): 822-835 (2019) - [c3]Binhang Yuan, Wenhui Xing:
Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks. MLMECH/CVII-STENT@MICCAI 2019: 36-44 - [i5]Binhang Yuan, Chen Wang, Fei Jiang, Mingsheng Long, Philip S. Yu, Yuan Liu:
WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection. CoRR abs/1902.05625 (2019) - [i4]Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao:
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning. CoRR abs/1904.11121 (2019) - [i3]Binhang Yuan, Wenhui Xing:
Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks. CoRR abs/1908.06802 (2019) - [i2]Binhang Yuan, Anastasios Kyrillidis, Christopher M. Jermaine:
Distributed Learning of Deep Neural Networks using Independent Subnet Training. CoRR abs/1910.02120 (2019) - 2018
- [c2]Jia Zou, R. Matthew Barnett, Tania Lorido-Botran, Shangyu Luo, Carlos Monroy, Sourav Sikdar, Kia Teymourian, Binhang Yuan, Chris Jermaine:
PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development. SIGMOD Conference 2018: 1189-1204 - 2017
- [j2]Binhang Yuan, Vijayaraghavan Murali, Christopher M. Jermaine:
Abridging source code. Proc. ACM Program. Lang. 1(OOPSLA): 58:1-58:26 (2017) - [i1]Jia Zou, R. Matthew Barnett, Tania Lorido-Botran, Shangyu Luo, Carlos Monroy, Sourav Sikdar, Kia Teymourian, Binhang Yuan, Chris Jermaine:
PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development. CoRR abs/1711.05573 (2017) - 2016
- [c1]Binhang Yuan, Ronald N. Goldman, Eric Wang, Olushola Olorunnipa, David Khechoyan:
Generating a 3D Normative Infant Cranial Model. ICCS 2016: 988-998 - 2014
- [j1]Bo Yan, Binhang Yuan, Bo Yang:
Effective Video Retargeting With Jittery Assessment. IEEE Trans. Multim. 16(1): 272-277 (2014)
Coauthor Index
Chris Jermaine
aka: Christopher M. Jermaine
aka: Christopher M. Jermaine
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