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2020 – today
- 2024
[c29]Hongli Mao, Xian-Ling Mao, Hanlin Tang, Yuming Shang, Heyan Huang:
Span Graph Transformer for Document-Level Named Entity Recognition. AAAI 2024: 18769-18777- [c28]Hongli Mao, Xian-Ling Mao, Hanlin Tang, Yu-Ming Shang, Xiaoyan Gao, Ao-Jie Ma, Heyan Huang:
Span-based Unified Named Entity Recognition Framework via Contrastive Learning. IJCAI 2024: 6406-6414 - [c27]Hanlin Tang, Zimeng Fang, Junyang He, Xue Zhou:
SPLICEGNN: SPLIt and ConnEct Tracklets in a Unified Graph Neural Network. PRCV (12) 2024: 315-329 - [i31]Hanlin Tang, Yifu Sun, Decheng Wu, Kai Liu, Jianchen Zhu, Zhanhui Kang:
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs. CoRR abs/2403.02775 (2024) - [i30]Hanlin Tang, Yang Lin, Jing Lin, Qingsen Han, Shikuan Hong, Yiwu Yao, Gongyi Wang:
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads. CoRR abs/2407.15891 (2024) - 2023
- [j4]Fan Zhao, Changgen Peng, Dequan Xu, Yicen Liu, Kun Niu, Hanlin Tang:
Attribute-based multi-user collaborative searchable encryption in COVID-19. Comput. Commun. 205: 118-126 (2023) - [j3]Yamin Hu, Hao Jiang, Hanlin Tang, Xin Lin, Zongyao Hu:
SQL#: A Language for Maintainable and Debuggable Database Queries. Int. J. Softw. Eng. Knowl. Eng. 33(5): 619-649 (2023) - [c26]Hanlin Tang, Yifu Sun, Decheng Wu, Kai Liu, Jianchen Zhu, Zhanhui Kang:
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs. EMNLP 2023: 9119-9128 - 2022
- [c25]Conglong Li, Ammar Ahmad Awan, Hanlin Tang, Samyam Rajbhandari, Yuxiong He:
1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. HIPC 2022: 272-281 - [i29]Hanlin Tang, Xipeng Zhang, Kai Liu, Jianchen Zhu, Zhanhui Kang:
MKQ-BERT: Quantized BERT with 4-bits Weights and Activations. CoRR abs/2203.13483 (2022) - 2021
- [c24]Weinan Zhang, Yue Zhang, Hanlin Tang, Zhengyu Zhao, Caihai Zhu, Ting Liu:
What Did You Refer to? Evaluating Co-References in Dialogue. ACL/IJCNLP (Findings) 2021: 5075-5084 - [c23]Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar:
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. ICLR 2021 - [c22]Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung:
On the geometry of generalization and memorization in deep neural networks. ICLR 2021 - [c21]Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He:
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. ICML 2021: 10118-10129 - [c20]Hanlin Tang, Yao Li, Ji Liu, Ming Yan:
ErrorCompensatedX: error compensation for variance reduced algorithms. NeurIPS 2021: 18102-18113 - [c19]Matteo Alleman, Jonathan Mamou, Miguel A. Del Rio, Hanlin Tang, Yoon Kim, SueYeon Chung
:
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models. RepL4NLP@ACL-IJCNLP 2021: 263-276 - [i28]Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He:
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. CoRR abs/2102.02888 (2021) - [i27]Conglong Li, Ammar Ahmad Awan, Hanlin Tang, Samyam Rajbhandari, Yuxiong He:
1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. CoRR abs/2104.06069 (2021) - [i26]Matteo Alleman, Jonathan Mamou, Miguel A. Del Rio, Hanlin Tang, Yoon Kim, SueYeon Chung:
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models. CoRR abs/2104.07578 (2021) - [i25]Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung:
On the geometry of generalization and memorization in deep neural networks. CoRR abs/2105.14602 (2021) - [i24]Hanlin Tang, Yao Li, Ji Liu, Ming Yan:
ErrorCompensatedX: error compensation for variance reduced algorithms. CoRR abs/2108.02102 (2021) - [i23]Weicong Ding, Hanlin Tang, Jingshuo Feng, Lei Yuan, Sen Yang, Guangxu Yang, Jie Zheng, Jing Wang, Qiang Su, Dong Zheng, Xuezhong Qiu, Yongqi Liu, Yuxuan Chen, Yang Liu, Chao Song, Dongying Kong, Kai Ren, Peng Jiang, Qiao Lian, Ji Liu:
PASTO: Strategic Parameter Optimization in Recommendation Systems - Probabilistic is Better than Deterministic. CoRR abs/2108.09076 (2021) - 2020
- [j2]Guangjie Li, Hui Liu, Ge Li, Sijie Shen, Hanlin Tang:
LSTM-based argument recommendation for non-API methods. Sci. China Inf. Sci. 63(9): 1-22 (2020) - [j1]Peter Mattson, Hanlin Tang, Gu-Yeon Wei, Carole-Jean Wu, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, David Kanter, Paulius Micikevicius, David A. Patterson, Guenther Schmuelling:
MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance. IEEE Micro 40(2): 8-16 (2020) - [c18]Barak Battash, Haim Barad, Hanlin Tang, Amit Bleiweiss:
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain. CVPR Workshops 2020: 2926-2934 - [c17]Léopold Cambier, Anahita Bhiwandiwalla, Ting Gong, Oguz H. Elibol, Mehran Nekuii, Hanlin Tang:
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks. ICLR 2020 - [c16]Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung:
Emergence of Separable Manifolds in Deep Language Representations. ICML 2020: 6713-6723 - [c15]Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou:
MLPerf Inference Benchmark. ISCA 2020: 446-459 - [c14]Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. MLSys 2020 - [c13]Shuyuan Li, Jianguo Li, Hanlin Tang, Rui Qian, Weiyao Lin:
ATRW: A Benchmark for Amur Tiger Re-identification in the Wild. ACM Multimedia 2020: 2590-2598 - [c12]Hongli Mao, Hanlin Tang, Wen Zhang, Heyan Huang, Xian-Ling Mao:
A Span-Based Distantly Supervised NER with Self-learning. NLPCC (1) 2020: 192-203 - [i22]Léopold Cambier, Anahita Bhiwandiwalla, Ting Gong, Mehran Nekuii, Oguz H. Elibol, Hanlin Tang:
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks. CoRR abs/2001.05674 (2020) - [i21]Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz H. Elibol, Hanlin Tang, Josh H. McDermott, SueYeon Chung:
Untangling in Invariant Speech Recognition. CoRR abs/2003.01787 (2020) - [i20]Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung:
Emergence of Separable Manifolds in Deep Language Representations. CoRR abs/2006.01095 (2020) - [i19]Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Hanlin Tang, Shie Mannor, Tamir Hazan, Somdeb Majumdar:
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. CoRR abs/2007.07298 (2020) - [i18]Hanlin Tang, Shaoduo Gan, Samyam Rajbhandari, Xiangru Lian, Ji Liu, Yuxiong He, Ce Zhang:
APMSqueeze: A Communication Efficient Adam-Preconditioned Momentum SGD Algorithm. CoRR abs/2008.11343 (2020)
2010 – 2019
- 2019
- [c11]Subarna Tripathi, Sharath Nittur Sridhar, Sairam Sundaresan, Hanlin Tang:
Compact Scene Graphs for Layout Composition and Patch Retrieval. CVPR Workshops 2019: 676-683 - [c10]Alexei Bastidas, Hanlin Tang:
Channel Attention Networks. CVPR Workshops 2019: 881-888 - [c9]Nicholas Weir, David Lindenbaum, Alexei Bastidas, Adam Van Etten, Varun Kumar Vijay, Sean McPherson, Jacob Shermeyer, Hanlin Tang:
SpaceNet MVOI: A Multi-View Overhead Imagery Dataset. ICCV 2019: 992-1001 - [c8]Brigit Schroeder, Subarna Tripathi, Hanlin Tang:
Triplet-Aware Scene Graph Embeddings. ICCV Workshops 2019: 1783-1787 - [c7]Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu:
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression. ICML 2019: 6155-6165 - [c6]Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu:
Distributed Learning over Unreliable Networks. ICML 2019: 7202-7212 - [c5]Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz H. Elibol, Hanlin Tang, Josh H. McDermott, SueYeon Chung:
Untangling in Invariant Speech Recognition. NeurIPS 2019: 14368-14378 - [i17]Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang:
Using Scene Graph Context to Improve Image Generation. CoRR abs/1901.03762 (2019) - [i16]Nicholas Weir, David Lindenbaum, Alexei Bastidas, Adam Van Etten, Sean McPherson, Jacob Shermeyer, Varun Kumar Vijay, Hanlin Tang:
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset. CoRR abs/1903.12239 (2019) - [i15]Subarna Tripathi, Sharath Nittur Sridhar, Sairam Sundaresan, Hanlin Tang:
Compact Scene Graphs for Layout Composition and Patch Retrieval. CoRR abs/1904.09348 (2019) - [i14]Hanlin Tang, Xiangru Lian, Tong Zhang, Ji Liu:
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. CoRR abs/1905.05957 (2019) - [i13]Shuyuan Li, Jianguo Li, Weiyao Lin, Hanlin Tang:
Amur Tiger Re-identification in the Wild. CoRR abs/1906.05586 (2019) - [i12]Varun Kumar Vijay, Abhinav Ganesh, Hanlin Tang, Arjun K. Bansal:
Generalization to Novel Objects using Prior Relational Knowledge. CoRR abs/1906.11315 (2019) - [i11]Hanlin Tang, Xiangru Lian, Shuang Qiu, Lei Yuan, Ce Zhang, Tong Zhang, Ji Liu:
DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. CoRR abs/1907.07346 (2019) - [i10]Brigit Schroeder, Subarna Tripathi, Hanlin Tang:
Triplet-Aware Scene Graph Embeddings. CoRR abs/1909.09256 (2019) - [i9]Brigit Schroeder, Hanlin Tang, Alexandre Alahi:
Using Image Priors to Improve Scene Understanding. CoRR abs/1910.01198 (2019) - [i8]Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. CoRR abs/1910.01500 (2019) - [i7]Chaoyang He, Conghui Tan, Hanlin Tang, Shuang Qiu, Ji Liu:
Central Server Free Federated Learning over Single-sided Trust Social Networks. CoRR abs/1910.04956 (2019) - [i6]Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou:
MLPerf Inference Benchmark. CoRR abs/1911.02549 (2019) - [i5]Barak Battash, Haim Barad, Hanlin Tang, Amit Bleiweiss:
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain. CoRR abs/1911.08206 (2019) - 2018
- [c4]Junhong Nie, Hanlin Tang, Jingxuan Wei:
Analysis on Convergence of Stochastic Processes in Cloud Computing Models. CIS 2018: 71-76 - [c3]Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu:
D2: Decentralized Training over Decentralized Data. ICML 2018: 4855-4863 - [c2]Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu:
Communication Compression for Decentralized Training. NeurIPS 2018: 7663-7673 - [i4]Hanlin Tang, Ce Zhang, Shaoduo Gan, Tong Zhang, Ji Liu:
Decentralization Meets Quantization. CoRR abs/1803.06443 (2018) - [i3]Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu:
D2: Decentralized Training over Decentralized Data. CoRR abs/1803.07068 (2018) - [i2]Hanlin Tang, Chen Yu, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang:
Distributed Learning over Unreliable Networks. CoRR abs/1810.07766 (2018) - 2017
- [i1]Hanlin Tang, Bill Lotter, Martin Schrimpf, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David D. Cox, Gabriel Kreiman:
Recurrent computations for visual pattern completion. CoRR abs/1706.02240 (2017) - 2016
- [c1]Hanlin Tang, Jedediah Singer, Matias Ison, Gnel Pivazyan, Melissa Romaine, Elizabeth Meller, Victoria Perron, Marlise Arlellano, Gabriel Kreiman, Adriana Boulin, Rosa Frias, James Carroll, Sarah Dowcett:
A machine learning approach to predict episodic memory formation. CISS 2016: 539-544
Coauthor Index
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