1 code implementation • 23 Apr 2024 • Yang Tan, Mingchen Li, Bingxin Zhou, Bozitao Zhong, Lirong Zheng, Pan Tan, Ziyi Zhou, Huiqun Yu, Guisheng Fan, Liang Hong
Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches.
no code implementations • 8 Jan 2024 • Long Deng, Ziqiang Li, Bingxin Zhou, Zhongming Chen, Ao Li, Yongxin Ge
Although few-shot action recognition based on metric learning paradigm has achieved significant success, it fails to address the following issues: (1) inadequate action relation modeling and underutilization of multi-modal information; (2) challenges in handling video matching problems with different lengths and speeds, and video matching problems with misalignment of video sub-actions.
no code implementations • 2 Oct 2023 • Outongyi Lv, Bingxin Zhou, Jing Wang, Xiang Xiao, Weishu Zhao, Lirong Zheng
Drawing inspiration from opinion dynamics in sociology, we propose ODNet, a novel message passing scheme incorporating bounded confidence, to refine the influence weight of local nodes for message propagation.
no code implementations • 5 Jul 2023 • Outongyi Lv, Bingxin Zhou
This study investigates the distribution of the Bellman approximation error through iterative exploration of the Bellman equation with the observation that the Bellman error approximately follows the Logistic distribution.
1 code implementation • NeurIPS 2023 • Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Liò, Yu Guang Wang
In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones.
no code implementations • 8 Jun 2023 • Yang Tan, Bingxin Zhou, Yuanhong Jiang, Yu Guang Wang, Liang Hong
Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions.
no code implementations • 13 Apr 2023 • Bingxin Zhou, Outongyi Lv, Kai Yi, Xinye Xiong, Pan Tan, Liang Hong, Yu Guang Wang
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications.
no code implementations • 5 Apr 2023 • Xinye Xiong, Bingxin Zhou, Yu Guang Wang
Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms.
no code implementations • 28 Feb 2023 • Xinliang Liu, Bingxin Zhou, Chutian Zhang, Yu Guang Wang
Graph neural networks (GNNs) have achieved champion in wide applications.
no code implementations • 17 Jun 2022 • Kai Yi, Jialin Chen, Yu Guang Wang, Bingxin Zhou, Pietro Liò, Yanan Fan, Jan Hamann
This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals.
1 code implementation • 15 Jun 2022 • Yiqing Shen, Bingxin Zhou, Xinye Xiong, Ruitian Gao, Yu Guang Wang
Existing solutions heavily rely on convolutional neural networks (CNNs) for global pixel-level analysis, leaving the underlying local geometric structure such as the interaction between cells in the tumor microenvironment unexplored.
1 code implementation • 30 May 2022 • Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.
1 code implementation • 10 Feb 2022 • Bingxin Zhou, Yuanhong Jiang, Yu Guang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, Xiaoqun Zhang
The performance of graph representation learning is affected by the quality of graph input.
1 code implementation • 15 Nov 2021 • Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Liò, Yuguang Wang
The architecture is assembled with a few simple effective computational blocks that constitute randomized SVD, MLP, and graph Framelet convolution.
1 code implementation • 5 Nov 2021 • Bingxin Zhou, Ruikun Li, Xuebin Zheng, Yu Guang Wang, Junbin Gao
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise.
no code implementations • ICLR Workshop GTRL 2021 • Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao
Geometric deep learning that employs the geometric and topological features of data has attracted increasing attention in deep neural networks.
1 code implementation • 13 Feb 2021 • Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yu Guang Wang, Pietro Lio, Ming Li, Guido Montufar
The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.
1 code implementation • 12 Dec 2020 • Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis.
no code implementations • 22 Jul 2020 • Xuebin Zheng, Bingxin Zhou, Ming Li, Yu Guang Wang, Junbin Gao
In this paper, we propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies.
no code implementations • 17 Jan 2020 • Bingxin Zhou, Xuebin Zheng, Junbin Gao
Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning.
no code implementations • 11 Feb 2019 • Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran, Richard Gerlach
Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings.