| Active bias: Training more accurate neural networks by emphasizing high variance samples HS Chang, E Learned-Miller, A McCallum Advances in Neural Information Processing Systems 30, 2017 | 405 | 2017 |
| Inorganic materials synthesis planning with literature-trained neural networks E Kim, Z Jensen, A van Grootel, K Huang, M Staib, S Mysore, HS Chang, ... Journal of chemical information and modeling 60 (3), 1194-1201, 2020 | 134 | 2020 |
| The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures S Mysore, Z Jensen, E Kim, K Huang, HS Chang, E Strubell, J Flanigan, ... arXiv preprint arXiv:1905.06939, 2019 | 124 | 2019 |
| Modeling Exercise Relationships in E-Learning: A Unified Approach HS Chang, HJ Hsu, KT Chen International Conference on Educational Data Mining (EDM), 2015 | 91 | 2015 |
| Autoknow: Self-driving knowledge collection for products of thousands of types XL Dong, X He, A Kan, X Li, Y Liang, J Ma, YE Xu, C Zhang, T Zhao, ... Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 90 | 2020 |
| Exploring visual and motion saliency for automatic video object extraction WT Li, HS Chang, KC Lien, HT Chang, YCF Wang IEEE Transactions on Image Processing 22 (7), 2600-2610, 2013 | 78 | 2013 |
| Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection HS Chang, ZY Wang, L Vilnis, A McCallum Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 72* | 2018 |
| Open aspect target sentiment classification with natural language prompts R Seoh, I Birle, M Tak, HS Chang, B Pinette, A Hough arXiv preprint arXiv:2109.03685, 2021 | 63 | 2021 |
| Automatically extracting action graphs from materials science synthesis procedures S Mysore, E Kim, E Strubell, A Liu, HS Chang, S Kompella, K Huang, ... arXiv preprint arXiv:1711.06872, 2017 | 46 | 2017 |
| Active learning for crowdsourced QoE modeling HS Chang, CF Hsu, T Hoßfeld, KT Chen IEEE Transactions on Multimedia 20 (12), 3337-3352, 2018 | 27 | 2018 |
| Optimizing the decomposition for multiple foreground cosegmentation HS Chang, YCF Wang Computer Vision and Image Understanding 141, 18-27, 2015 | 27 | 2015 |
| Using error decay prediction to overcome practical issues of deep active learning for named entity recognition HS Chang, S Vembu, S Mohan, R Uppaal, A McCallum Machine Learning 109, 1749-1778, 2020 | 26 | 2020 |
| Softmax bottleneck makes language models unable to represent multi-mode word distributions HS Chang, A McCallum Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 19 | 2022 |
| Changing the mind of transformers for topically-controllable language generation HS Chang, J Yuan, M Iyyer, A McCallum arXiv preprint arXiv:2103.15335, 2021 | 16 | 2021 |
| Superpixel-based large displacement optical flow HS Chang, YCF Wang 2013 IEEE international conference on image processing, 3835-3839, 2013 | 15 | 2013 |
| Efficient graph-based word sense induction by distributional inclusion vector embeddings HS Chang, A Agrawal, A Ganesh, A Desai, V Mathur, A Hough, ... arXiv preprint arXiv:1804.03257, 2018 | 13 | 2018 |
| Extending multi-sense word embedding to phrases and sentences for unsupervised semantic applications HS Chang, A Agrawal, A McCallum Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6956-6965, 2021 | 12 | 2021 |
| Multi-CLS BERT: An efficient alternative to traditional ensembling HS Chang, RY Sun, K Ricci, A McCallum arXiv preprint arXiv:2210.05043, 2022 | 11 | 2022 |
| Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema. HS Chang, A Munir, A Liu, JTZ Wei, A Traylor, A Nagesh, N Monath, ... TAC, 2016 | 11 | 2016 |
| Revisiting the architectures like pointer networks to efficiently improve the next word distribution, summarization factuality, and beyond HS Chang, Z Yao, A Gon, H Yu, A McCallum arXiv preprint arXiv:2305.12289, 2023 | 7 | 2023 |