Hong-Min Chu

According to our database, Hong-Min Chu authored at least 14 papers between 2016 and 2023.

Collaborative distances:

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

2023

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

CoRR, 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

Universal Guidance for Diffusion Models.

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

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

2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2022

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