no code implementations • 27 Feb 2024 • Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu
Designing molecules with desirable properties, such as drug-likeliness and high binding affinities towards protein targets, is a challenging problem.
no code implementations • 7 Feb 2024 • Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu
We introduce the Latent Plan Transformer (LPT), a novel model that leverages a latent space to connect a Transformer-based trajectory generator and the final return.
no code implementations • 5 Oct 2023 • Deqian Kong, Yuhao Huang, Jianwen Xie, Ying Nian Wu
This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design, where the goal is to find molecules with optimal values of a target chemical or biological property that can be computed by an existing software.
1 code implementation • 9 Jun 2023 • Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu
To search for molecules with desired properties, we propose a sampling with gradual distribution shifting (SGDS) algorithm, so that after learning the model initially on the training data of existing molecules and their properties, the proposed algorithm gradually shifts the model distribution towards the region supported by molecules with desired values of properties.
1 code implementation • 1 Jun 2023 • Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu
The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system.
no code implementations • 5th Workshop on Meta-Learning at NeurIPS 2021 2021 • Deqian Kong, Bo Pang, Ying Nian Wu
We propose to learn an energy-based model (EBM) in the latent space of a top-down generative model such that the EBM in the low dimensional latent space is able to be learned efficiently and adapt to each task rapidly.
no code implementations • ICCV 2021 • Yixin Chen, Qing Li, Deqian Kong, Yik Lun Kei, Song-Chun Zhu, Tao Gao, Yixin Zhu, Siyuan Huang
To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction.