Hong-Min Chu

According to our database, Hong-Min Chu authored at least 13 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Universal Guidance for Diffusion Models.

Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021

Active Learning at the ImageNet Scale.

Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein

CoRR, 2021

WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic.

Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic.

Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

CoRR, 2020

2019

Dynamic principal projection for cost-sensitive online multi-label classification.

Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin

Mach. Learn., 2019

Deep Learning with a Rethinking Structure for Multi-label Classification.

Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin

Proceedings of The 11th Asian Conference on Machine Learning, 2019

2018

Robust Distributed Anomaly Detection Using Optimal Weighted One-Class Random Forests.

Yu-Lin Tsou, Hong-Min Chu, Cong Li, Shao-Wen Yang

Proceedings of the IEEE International Conference on Data Mining, 2018

Deep Generative Models for Weakly-Supervised Multi-Label Classification.

Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang

Proceedings of the Computer Vision - ECCV 2018, 2018

Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions.

Hong-Min Chu, Shao-Wen Yang, Padmanabhan Pillai, Yen-Kuang Chen

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2016

Can Active Learning Experience Be Transferred?

Hong-Min Chu, Hsuan-Tien Lin

Proceedings of the IEEE 16th International Conference on Data Mining, 2016