Kaizhao Liang

According to our database, Kaizhao Liang authored at least 15 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Memory-Efficient LLM Training with Online Subspace Descent.

Kaizhao Liang, Bo Liu, Lizhang Chen, Qiang Liu

CoRR, 2024

SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts.

Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi, Yun Du, Mingran Wang, Xiangyu Song, Kejie Zhang, Tianren Gao, Angela Wang, Karen Li, Yongning Sheng, Joshua Brot, Denis Sokolov, Apurv Vivek, Calvin Leung, Arjun Sabnis, Jiayu Bai, Tuowen Zhao, Mark Gottscho, David Jackson, Mark Luttrell, Manish K. Shah, Edison Chen, Kaizhao Liang, Swayambhoo Jain, Urmish Thakker, Dawei Huang, Sumti Jairath, Kevin J. Brown, Kunle Olukotun

CoRR, 2024

Communication Efficient Distributed Training with Distributed Lion.

Bo Liu, Lemeng Wu, Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu

CoRR, 2024

A Survey on Multimodal Large Language Models for Autonomous Driving.

Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2024

Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts.

Lizhang Chen, Bo Liu, Kaizhao Liang, Qiang Liu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding.

Xu Cao, Tong Zhou, Yunsheng Ma, Wenqian Ye, Can Cui, Kun Tang, Zhipeng Cao, Kaizhao Liang, Ziran Wang, James M. Rehg, Chao Zheng

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

PIE: Simulating Disease Progression via Progressive Image Editing.

Kaizhao Liang, Xu Cao, Kuei-Da Liao, Tianren Gao, Wenqian Ye, Zhengyu Chen, Jianguo Cao, Tejas Nama, Jimeng Sun

CoRR, 2023

A Unified Knowledge Distillation Framework for Deep Directed Graphical Models.

Yizhuo Chen, Kaizhao Liang, Zhe Zeng, Shuochao Yao, Huajie Shao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models.

Beidi Chen, Tri Dao, Kaizhao Liang, Jiaming Yang, Zhao Song, Atri Rudra, Christopher Ré

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation.

Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard H. Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicholas Roberts, Juan Diego Rodriguez, Claude Roux, Paulo Henrique Santos Vasconcellos, Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib Shamsi, Xudong Shen, Yiwen Shi, Haoyue Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, K. V. Aditya Srivatsa, Tony Sun, Mukund Varma T., A. Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Zijie J. Wang, Gloria Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmanski, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

CoRR, 2021

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability.

Kaizhao Liang, Jacky Y. Zhang, Boxin Wang, Zhuolin Yang, Sanmi Koyejo, Bo Li

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Does Adversarial Transferability Indicate Knowledge Transferability?

Kaizhao Liang, Jacky Y. Zhang, Oluwasanmi Koyejo, Bo Li

CoRR, 2020

Adversarial Mutual Information for Text Generation.

Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li

Proceedings of the 37th International Conference on Machine Learning, 2020

Unrestricted Adversarial Examples via Semantic Manipulation.

Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, David A. Forsyth

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Big but Imperceptible Adversarial Perturbations via Semantic Manipulation.

Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, David A. Forsyth

CoRR, 2019