Hong-Min Chu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 20

Top Topics

Active Learning

Diffusion Models

Learning Experience

Top Venues

Publications

Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. ICLR (2024)
Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR (2024)
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. CVPR Workshops (2023)
Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. ICLR (2023)
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. NeurIPS (2023)
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. CoRR (2023)
Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR (2023)
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. CoRR (2022)
Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-Yeh Chiang, Christoph Studer, Tom Goldstein
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. ICLR (2021)
Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein
Active Learning at the ImageNet Scale. CoRR (2021)
Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-Yeh Chiang, Christoph Studer, Tom Goldstein
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. CoRR (2020)
Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin
Deep Learning with a Rethinking Structure for Multi-label Classification. ACML (2019)
Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin
Dynamic principal projection for cost-sensitive online multi-label classification. Mach. Learn. 108 (8-9) (2019)
Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin
Deep Learning with a Rethinking Structure for Multi-label Classification. CoRR (2018)
Yu-Lin Tsou, Hong-Min Chu, Cong Li, Shao-Wen Yang
Robust Distributed Anomaly Detection Using Optimal Weighted One-Class Random Forests. ICDM (2018)
Hong-Min Chu, Shao-Wen Yang, Padmanabhan Pillai, Yen-Kuang Chen
Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions. AAAI (2018)
Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang
Deep Generative Models for Weakly-Supervised Multi-Label Classification. ECCV (2) (2018)
Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification. CoRR (2017)
Hong-Min Chu, Hsuan-Tien Lin
Can Active Learning Experience Be Transferred? ICDM (2016)
Hong-Min Chu, Hsuan-Tien Lin
Can Active Learning Experience Be Transferred? CoRR (2016)