| Cold diffusion: Inverting arbitrary image transforms without noise A Bansal, E Borgnia, HM Chu, J Li, H Kazemi, F Huang, M Goldblum, ... Advances in Neural Information Processing Systems 36, 41259-41282, 2023 | 266 | 2023 |
| Universal guidance for diffusion models A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 252 | 2023 |
| Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... arXiv preprint arXiv:2310.05914, 2023 | 70* | 2023 |
| Unsupervised machine learning ensemble for anomaly detection HM Chu, YL Tsou, SW Yang US Patent App. 15/283,308, 2018 | 57 | 2018 |
| Deep generative models for weakly-supervised multi-label classification HM Chu, CK Yeh, YCF Wang Proceedings of the european conference on computer vision (ECCV), 400-415, 2018 | 48 | 2018 |
| Can active learning experience be transferred? HM Chu, HT Lin 2016 IEEE 16th international conference on data mining (ICDM), 841-846, 2016 | 43 | 2016 |
| Deep learning with a rethinking structure for multi-label classification YY Yang, YA Lin, HM Chu, HT Lin Asian Conference on Machine Learning, 125-140, 2019 | 42 | 2019 |
| Entropy-based weighting in random forest models YL Tsou, SW Yang, HM Chu US Patent 11,315,045, 2022 | 38 | 2022 |
| Active learning at the imagenet scale ZAS Emam, HM Chu, PY Chiang, W Czaja, R Leapman, M Goldblum, ... arXiv preprint arXiv:2111.12880, 2021 | 35 | 2021 |
| Wrapnet: Neural net inference with ultra-low-resolution arithmetic R Ni, H Chu, O Castañeda, P Chiang, C Studer, T Goldstein arXiv preprint arXiv:2007.13242, 2020 | 31* | 2020 |
| Robust distributed anomaly detection using optimal weighted one-class random forests YL Tsou, HM Chu, C Li, SW Yang 2018 IEEE International Conference on Data Mining (ICDM), 1272-1277, 2018 | 17 | 2018 |
| Scheduling in visual fog computing: NP-completeness and practical efficient solutions HM Chu, SW Yang, P Pillai, YK Chen Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 17 | 2018 |
| Distributed assignment of video analytics tasks in cloud computing environments to reduce bandwidth utilization HM Chu, SW Yang, YK Chen US Patent 10,567,248, 2020 | 14 | 2020 |
| Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2023. Universal guidance for diffusion models A Bansal, HM Chu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 0 | 14 | |
| A patient‐oriented, general‐practitioner‐level, deep‐learning‐based cutaneous pigmented lesion risk classifier on a smartphone YPH Chin, ZY Hou, MY Lee, HM Chu, HH Wang, YT Lin, A Gittin, ... British Journal of Dermatology 182 (6), 1498-1500, 2020 | 13 | 2020 |
| Panning for gold in federated learning: Targeted text extraction under arbitrarily large-scale aggregation HM Chu, J Geiping, LH Fowl, M Goldblum, T Goldstein The Eleventh International Conference on Learning Representations, 2022 | 12 | 2022 |
| Avi Schwarzschild, Aniruddha Saha, et al. Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... arXiv preprint arXiv:2310.05914, 2023 | 11 | 2023 |
| Avi Schwarzschild, Aniruddha Saha, et al. 2023. Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... arXiv preprint arXiv:2310.05914, 2023 | 7 | 2023 |
| Dynamic principal projection for cost-sensitive online multi-label classification HM Chu, KH Huang, HT Lin Machine Learning 108, 1193-1230, 2019 | 7 | 2019 |
| Gradient-based Optimization of Dataset Mixtures HM Chu, DS Kalra, A Bansal, J Kirchenbauer, J Geiping, T Goldstein | | |
