no code implementations • 15 May 2024 • Shikun Feng, Yuyan Ni, Minghao Li, Yanwen Huang, Zhi-Ming Ma, Wei-Ying Ma, Yanyan Lan
Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP.
2 code implementations • 18 Apr 2024 • Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, Wei-Ying Ma
Generative models for structure-based drug design (SBDD) have shown promising results in recent years.
1 code implementation • 17 Mar 2024 • Yuxuan Song, Jingjing Gong, Yanru Qu, Hao Zhou, Mingyue Zheng, Jingjing Liu, Wei-Ying Ma
Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.
1 code implementation • 5 Mar 2024 • Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou
In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling.
no code implementations • 4 Mar 2024 • Bowen Gao, Minsi Ren, Yuyan Ni, Yanwen Huang, Bo Qiang, Zhi-Ming Ma, Wei-Ying Ma, Yanyan Lan
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score.
no code implementations • 21 Feb 2024 • Han Tang, Shikun Feng, Bicheng Lin, Yuyan Ni, Jingjing Liu, Wei-Ying Ma, Yanyan Lan
REMO offers a novel solution to MRL by exploiting the underlying shared patterns in chemical reactions as \textit{context} for pre-training, which effectively infers meaningful representations of common chemistry knowledge.
1 code implementation • 12 Dec 2023 • Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates).
no code implementations • 3 Nov 2023 • Yuyan Ni, Shikun Feng, Wei-Ying Ma, Zhi-Ming Ma, Yanyan Lan
By aligning with physical principles, SliDe shows a 42\% improvement in the accuracy of estimated force fields compared to current state-of-the-art denoising methods, and thus outperforms traditional baselines on various molecular property prediction tasks.
1 code implementation • 20 Jul 2023 • Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma
Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks.
no code implementations • 18 Oct 2022 • Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang, Kang Liu, ZhiMing Ma, Wei-Ying Ma, Yanyan Lan
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
no code implementations • ICLR 2022 • Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng
Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner.
2 code implementations • CVPR 2020 • Yifang Men, Yiming Mao, Yuning Jiang, Wei-Ying Ma, Zhouhui Lian
This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e. g., pose, head, upper clothes and pants) provided in various source inputs.
Ranked #6 on Pose Transfer on Deep-Fashion
no code implementations • 5 Sep 2019 • Zhichen Zhao, Bo-Wen Zhang, Yuning Jiang, Li Xu, Lei LI, Wei-Ying Ma
However, the datasets from source domain are simply discarded in the fine-tuning process.
1 code implementation • 11 Apr 2019 • Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei LI, Weiwei Sun, Wei-Ying Ma
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
no code implementations • 28 Aug 2017 • Yalong Bai, Kuiyuan Yang, Tao Mei, Wei-Ying Ma, Tiejun Zhao
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.
1 code implementation • 25 Jan 2017 • Chen Xing, Wei Wu, Yu Wu, Ming Zhou, YaLou Huang, Wei-Ying Ma
With the word level attention, hidden vectors of a word level encoder are synthesized as utterance vectors and fed to an utterance level encoder to construct hidden representations of the context.
1 code implementation • NeurIPS 2016 • Yingce Xia, Di He, Tao Qin, Li-Wei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma
Based on the feedback signals generated during this process (e. g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e. g., using the policy gradient methods).
1 code implementation • 21 Jun 2016 • Chen Xing, Wei Wu, Yu Wu, Jie Liu, YaLou Huang, Ming Zhou, Wei-Ying Ma
We consider incorporating topic information into the sequence-to-sequence framework to generate informative and interesting responses for chatbots.
1 code implementation • 4 Dec 2014 • Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric P. Xing, Tie-Yan Liu, Wei-Ying Ma
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers.
no code implementations • 17 Dec 2013 • Yalong Bai, Kuiyuan Yang, Wei Yu, Wei-Ying Ma, Tiejun Zhao
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.