Chu, Hong-Min

11 publications

ICLR 2024 NEFTune: Noisy Embeddings Improve Instruction Finetuning 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

ICLR 2024 Universal Guidance for Diffusion Models Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Roni Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

NeurIPS 2023 Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

ICLR 2023 Panning for Gold in Federated Learning: Targeted Text Extraction Under Arbitrarily Large-Scale Aggregation Hong-Min Chu, Jonas Geiping, Liam H Fowl, Micah Goldblum, Tom Goldstein

CVPRW 2023 Universal Guidance for Diffusion Models Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

NeurIPSW 2022 Panning for Gold in Federated Learning: Targeted Text Extraction Under Arbitrarily Large-Scale Aggregation Hong-Min Chu, Jonas Geiping, Liam H Fowl, Micah Goldblum, Tom Goldstein

ICLR 2021 WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic Renkun Ni, Hong-min Chu, Oscar Castaneda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

ACML 2019 Deep Learning with a Rethinking Structure for Multi-Label Classification Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin

MLJ 2019 Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin

ECCV 2018 Deep Generative Models for Weakly-Supervised Multi-Label Classification Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang

AAAI 2018 Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions Hong-Min Chu, Shao-Wen Yang, Padmanabhan Pillai, Yen-Kuang Chen