Deep Learning for Extreme Multi-label Text Classification J Liu, WC Chang, Y Wu, Y Yang Proceedings of the 40th International ACM SIGIR Conference on Research and …, 2017 | 832 | 2017 |
Analogical inference for multi-relational embeddings H Liu, Y Wu, Y Yang Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 491 | 2017 |
Large language models can self-improve J Huang, SS Gu, L Hou, Y Wu, X Wang, H Yu, J Han arXiv preprint arXiv:2210.11610, 2022 | 445 | 2022 |
Review networks for caption generation Z Yang, Y Yuan, Y Wu, WW Cohen, RR Salakhutdinov Advances in neural information processing systems 29, 2016 | 323 | 2016 |
Storygan: A sequential conditional gan for story visualization Y Li, Z Gan, Y Shen, J Liu, Y Cheng, Y Wu, L Carin, D Carlson, J Gao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 239 | 2019 |
Deep learning for epidemiological predictions Y Wu, Y Yang, H Nishiura, M Saitoh The 41st International ACM SIGIR Conference on Research & Development in …, 2018 | 131 | 2018 |
Knowledge embedding based graph convolutional network D Yu, Y Yang, R Zhang, Y Wu Proceedings of the Web Conference 2021, 1619-1628, 2021 | 126 | 2021 |
Graph-revised convolutional network D Yu, R Zhang, Z Jiang, Y Wu, Y Yang Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 112 | 2021 |
Unsupervised Cross-lingual Transfer of Word Embedding Spaces R Xu, Y Yang, N Otani, Y Wu Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 98 | 2018 |
Active Learning for Graph Neural Networks via Node Feature Propagation Y Wu, Y Xu, A Singh, Y Yang, A Dubrawski arXiv preprint arXiv:1910.07567, 2019 | 70 | 2019 |
Graph Convolutional Matrix Completion for Bipartite Edge Prediction Y Wu, H Liu, Y Yang International Joint Conference on Knowledge Discovery, Knowledge Engineering …, 2018 | 68 | 2018 |
Switch-based active deep dyna-q: Efficient adaptive planning for task-completion dialogue policy learning Y Wu, X Li, J Liu, J Gao, Y Yang Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7289-7296, 2019 | 50 | 2019 |
Token Dropping for Efficient BERT Pretraining L Hou, RY Pang, T Zhou, Y Wu, X Song, X Song, D Zhou Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 44 | 2022 |
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference T Lei, J Bai, S Brahma, J Ainslie, K Lee, Y Zhou, N Du, VY Zhao, Y Wu, ... arXiv preprint arXiv:2304.04947, 2023 | 43 | 2023 |
Cross-domain kernel induction for transfer learning Y Wu, WC Chang, H Liu, Y Yang Thirty-First AAAI Conference on Artificial Intelligence, 2017 | 33 | 2017 |
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts S Shen, L Hou, Y Zhou, N Du, S Longpre, J Wei, HW Chung, B Zoph, ... arXiv preprint arXiv:2305.14705, 2023 | 27 | 2023 |
Provable stochastic optimization for global contrastive learning: Small batch does not harm performance Z Yuan, Y Wu, ZH Qiu, X Du, L Zhang, D Zhou, T Yang International Conference on Machine Learning, 25760-25782, 2022 | 24 | 2022 |
A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data S Li, F Dong, Y Wu, S Zhang, C Zhang, X Liu, T Jiang, J Zeng Nucleic acids research 45 (14), e129-e129, 2017 | 22 | 2017 |
Computational protein design using AND/OR branch-and-bound search Y Zhou, Y Wu, J Zeng Journal of Computational Biology 23 (6), 439-451, 2016 | 13 | 2016 |
Multi-step problem solving through a verifier: An empirical analysis on model-induced process supervision Z Wang, Y Li, Y Wu, L Luo, L Hou, H Yu, J Shang arXiv preprint arXiv:2402.02658, 2024 | 11 | 2024 |