| Yourefit: Embodied reference understanding with language and gesture Y Chen, Q Li, D Kong, YL Kei, SC Zhu, T Gao, Y Zhu, S Huang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 32 | 2021 |
| A benchmark dataset and multi-scale attention network for semantic traffic light detection Y Feng, D Kong, P Wei, H Sun, N Zheng 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 1-8, 2019 | 8 | 2019 |
| Unsupervised meta-learning via latent space energy-based model of symbol vector coupling D Kong, B Pang, YN Wu Fifth Workshop on Meta-Learning at the Conference on Neural Information …, 2021 | 5 | 2021 |
| Molecule design by latent space energy-based modeling and gradual distribution shifting D Kong, B Pang, T Han, YN Wu Uncertainty in Artificial Intelligence, 1109-1120, 2023 | 4 | 2023 |
| Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference Y Xu, D Kong, D Xu, Z Ji, B Pang, P Fung, YN Wu International Conference on Machine Learning, 2023 | 3 | 2023 |
| Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space P Yu, D Zhang, H He, X Ma, R Miao, Y Lu, Y Zhang, D Kong, R Gao, J Xie, ... arXiv preprint arXiv:2405.16730, 2024 | 1 | 2024 |
| Molecule Design by Latent Prompt Transformer D Kong, Y Huang, J Xie, YN Wu NeurIPS 2023 AI for Science Workshop., 2023 | 1 | 2023 |
| A statistical approach to topological entanglement: Boltzmann machine representation of high-order irreducible correlation S Feng, D Kong, N Trivedi arXiv preprint arXiv:2302.03212, 2023 | 1 | 2023 |
| Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer D Kong, Y Huang, J Xie, E Honig, M Xu, S Xue, P Lin, S Zhou, S Zhong, ... arXiv preprint arXiv:2402.17179, 2024 | | 2024 |
| Latent Plan Transformer: Planning as Latent Variable Inference D Kong, D Xu, M Zhao, B Pang, J Xie, A Lizarraga, Y Huang, S Xie, ... arXiv preprint arXiv:2402.04647, 2024 | | 2024 |
| Long-term social interaction context: The key to egocentric addressee detection D Kong, F Khan, X Zhang, P Singhal, YN Wu ICASSP 2024, 2024 | | 2024 |
| Learning Energy-Based Prior Model for Unsupervised Meta-Learning D Kong University of California, Los Angeles, 2021 | | 2021 |
Ying Nian WuUCLA Department of Statistics在 stat.ucla.edu 的电子邮件经过验证
