no code implementations • 30 Sep 2021 • Luyao Yuan, Zipeng Fu, Linqi Zhou, Kexin Yang, Song-Chun Zhu
Currently, in the study of multiagent systems, the intentions of agents are usually ignored.
no code implementations • ICCV 2021 • Linqi Zhou, Yilun Du, Jiajun Wu
We propose a novel approach for probabilistic generative modeling of 3D shapes.
1 code implementation • ACL 2020 • Bo Pang, Erik Nijkamp, Wenjuan Han, Linqi Zhou, Yixian Liu, Kewei Tu
Open-domain dialogue generation has gained increasing attention in Natural Language Processing.
no code implementations • CVPR 2020 • Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM).
no code implementations • ECCV 2020 • Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu
Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.
no code implementations • 19 Nov 2019 • Dandan Zhu, Tian Han, Linqi Zhou, Xiaokang Yang, Ying Nian Wu
We propose the clustered generator model for clustering which contains both continuous and discrete latent variables.
1 code implementation • 1 Sep 2019 • Zijun Zhang, Linqi Zhou, Liangke Gou, Ying Nian Wu
We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep learning models.