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2020 – today
- 2024
[c11]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. ICLR 2024- [c10]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 - 2023
- [c9]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CVPR Workshops 2023: 843-852 - [c8]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 - [c7]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 - [i8]Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Universal Guidance for Diffusion Models. CoRR abs/2302.07121 (2023) - [i7]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 abs/2310.05914 (2023) - 2022
- [i6]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 abs/2208.09392 (2022) - 2021
- [c6]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 - [i5]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 abs/2111.12880 (2021) - 2020
- [i4]Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein:
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. CoRR abs/2007.13242 (2020)
2010 – 2019
- 2019
- [j1]Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin:
Dynamic principal projection for cost-sensitive online multi-label classification. Mach. Learn. 108(8-9): 1193-1230 (2019) - [c5]Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin:
Deep Learning with a Rethinking Structure for Multi-label Classification. ACML 2019: 125-140 - 2018
- [c4]Hong-Min Chu, Shao-Wen Yang, Padmanabhan Pillai, Yen-Kuang Chen:
Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions. AAAI 2018: 6127-6135 - [c3]Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang:
Deep Generative Models for Weakly-Supervised Multi-Label Classification. ECCV (2) 2018: 409-425 - [c2]Yu-Lin Tsou, Hong-Min Chu, Cong Li, Shao-Wen Yang:
Robust Distributed Anomaly Detection Using Optimal Weighted One-Class Random Forests. ICDM 2018: 1272-1277 - [i3]Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin:
Deep Learning with a Rethinking Structure for Multi-label Classification. CoRR abs/1802.01697 (2018) - 2017
- [i2]Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin:
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification. CoRR abs/1711.05060 (2017) - 2016
- [c1]Hong-Min Chu, Hsuan-Tien Lin:
Can Active Learning Experience Be Transferred? ICDM 2016: 841-846 - [i1]Hong-Min Chu, Hsuan-Tien Lin:
Can Active Learning Experience Be Transferred? CoRR abs/1608.00667 (2016)
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