Search Results for author: Yu Guang Wang

Found 34 papers, 18 papers with code

Layer-diverse Negative Sampling for Graph Neural Networks

no code implementations18 Mar 2024 Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan

Experiments on various real-world graph datasets demonstrate the effectiveness of our approach in improving the diversity of negative samples and overall learning performance.

Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation

no code implementations9 Nov 2023 Jialin Chen, Yuelin Wang, Cristian Bodnar, Rex Ying, Pietro Lio, Yu Guang Wang

However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue.

Node Classification

Graph Denoising Diffusion for Inverse Protein Folding

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.

Denoising Protein Folding

Multi-level Protein Representation Learning for Blind Mutational Effect Prediction

no code implementations8 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.

Protein Folding Representation Learning +1

Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks

no code implementations13 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.

Graph Representation Learning

Graph Representation Learning for Interactive Biomolecule Systems

no code implementations5 Apr 2023 Xinye Xiong, Bingxin Zhou, Yu Guang Wang

Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms.

Drug Discovery Graph Representation Learning

EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning

1 code implementation CVPR 2023 Chenxin Xu, Robby T. Tan, Yuhong Tan, Siheng Chen, Yu Guang Wang, Xinchao Wang, Yanfeng Wang

In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle.

Human Pose Forecasting motion prediction +2

Framelet Message Passing

no code implementations28 Feb 2023 Xinliang Liu, Bingxin Zhou, Chutian Zhang, Yu Guang Wang

Graph neural networks (GNNs) have achieved champion in wide applications.

Node Classification

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

no code implementations29 Dec 2022 Mingchen Li, Liqi Kang, Yi Xiong, Yu Guang Wang, Guisheng Fan, Pan Tan, Liang Hong

Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism.

Data Augmentation

Oversquashing in GNNs through the lens of information contraction and graph expansion

1 code implementation6 Aug 2022 Pradeep Kr. Banerjee, Kedar Karhadkar, Yu Guang Wang, Uri Alon, Guido Montúfar

We compare the spectral expansion properties of our algorithm with that of an existing curvature-based non-local rewiring strategy.

graph construction

Approximate Equivariance SO(3) Needlet Convolution

no code implementations17 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.

Quantum Chemistry Regression

How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images

1 code implementation15 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.

ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition

1 code implementation11 Jun 2022 Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, Shi Jin

Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next.

Node Classification

Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks

no code implementations1 Jun 2022 Hao Chen, Yu Guang Wang, Huan Xiong

In particular, we obtain an optimal upper bound for the maximum number of linear regions for one-layer GCNs, and the upper and lower bounds for multi-layer GCNs.

Embedding Graphs on Grassmann Manifold

1 code implementation30 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.

Graph Embedding Graph Property Prediction +2

Graph Denoising with Framelet Regularizer

1 code implementation5 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.

Denoising

Anomaly Detection in Dynamic Graphs via Transformer

1 code implementation18 Jun 2021 Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent CS Lee

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.

Anomaly Detection

Grassmann Graph Embedding

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.

Dimensionality Reduction Graph Embedding

How Framelets Enhance Graph Neural Networks

1 code implementation13 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.

Denoising

Decimated Framelet System on Graphs and Fast G-Framelet Transforms

1 code implementation12 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.

Graph Classification Graph Representation Learning +1

How Powerful are Shallow Neural Networks with Bandlimited Random Weights?

no code implementations19 Aug 2020 Ming Li, Sho Sonoda, Feilong Cao, Yu Guang Wang, Jiye Liang

Despite the well-known fact that a neural network is a universal approximator, in this study, we mathematically show that when hidden parameters are distributed in a bounded domain, the network may not achieve zero approximation error.

Learning Theory

MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning

no code implementations22 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.

Graph Classification

Distributed Learning via Filtered Hyperinterpolation on Manifolds

no code implementations18 Jul 2020 Guido Montúfar, Yu Guang Wang

Learning mappings of data on manifolds is an important topic in contemporary machine learning, with applications in astrophysics, geophysics, statistical physics, medical diagnosis, biochemistry, 3D object analysis.

Geophysics Medical Diagnosis

Path Integral Based Convolution and Pooling for Graph Neural Networks

1 code implementation NeurIPS 2020 Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Lio

Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regression tasks on graphs.

Graph Classification Graph Regression +1

Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus

2 code implementations31 Jan 2020 Nicole Hallett, Kai Yi, Josef Dick, Christopher Hodge, Gerard Sutton, Yu Guang Wang, Jingjing You

Currently, there is no cure for keratoconus other than corneal transplantation for advanced stage keratoconus or corneal cross-linking, which can only halt KC progression.

General Classification

CosmoVAE: Variational Autoencoder for CMB Image Inpainting

1 code implementation31 Jan 2020 Kai Yi, Yi Guo, Yanan Fan, Jan Hamann, Yu Guang Wang

The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters.

Image Inpainting

Distributed filtered hyperinterpolation for noisy data on the sphere

no code implementations6 Oct 2019 Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou

This paper develops distributed filtered hyperinterpolation for noisy data on the sphere, which assigns the data fitting task to multiple servers to find a good approximation of the mapping of input and output data.

Geophysics Model Selection

Haar Graph Pooling

1 code implementation ICML 2020 Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan

Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks.

General Classification Graph Classification +1

HaarPooling: Graph Pooling with Compressive Haar Basis

no code implementations25 Sep 2019 Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan

The input of each pooling layer is transformed by the compressive Haar basis of the corresponding clustering.

Graph Classification

FaVeST: Fast Vector Spherical Harmonic Transforms

2 code implementations31 Jul 2019 Quoc T. Le Gia, Ming Li, Yu Guang Wang

The forward FaVeST evaluates the Fourier coefficients and has a computational cost proportional to $N\log \sqrt{N}$ for $N$ number of evaluation points.

Numerical Analysis Computational Complexity Numerical Analysis 65T50, 37C10, 33C55, 65D30 F.2.1; G.4; G.1.4; G.1.2

Fast Haar Transforms for Graph Neural Networks

no code implementations10 Jul 2019 Ming Li, Zheng Ma, Yu Guang Wang, Xiaosheng Zhuang

Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data.

General Classification Node Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.