Search Results for author: Wee Peng Tay

Found 50 papers, 13 papers with code

Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study

no code implementations9 Jan 2024 Qiyu Kang, Kai Zhao, Yang song, Yihang Xie, Yanan Zhao, Sijie Wang, Rui She, Wee Peng Tay

In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models.

PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations

no code implementations6 Jan 2024 Rui She, Sijie Wang, Qiyu Kang, Kai Zhao, Yang song, Wee Peng Tay, Tianyu Geng, Xingchao Jian

We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds.

Point Cloud Registration Position

DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition

1 code implementation17 Dec 2023 Sijie Wang, Rui She, Qiyu Kang, Xingchao Jian, Kai Zhao, Yang song, Wee Peng Tay

The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts.

Knowledge Distillation Visual Place Recognition

Towards Neuromorphic Compression based Neural Sensing for Next-Generation Wireless Implantable Brain Machine Interface

no code implementations15 Dec 2023 Vivek Mohan, Wee Peng Tay, Arindam Basu

Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection.

Data Compression

Sparse graph sequences, generalized graphons and signal processing

no code implementations13 Dec 2023 Feng Ji, Xingchao Jian, Wee Peng Tay

Recently, graphon signal processing has been developed to study large graphs from the signal processing perspective.

Image Patch-Matching with Graph-Based Learning in Street Scenes

no code implementations8 Nov 2023 Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay, Yong Liang Guan, Diego Navarro Navarro, Andreas Hartmannsgruber

Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving.

Autonomous Driving Metric Learning +1

RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments

1 code implementation7 Nov 2023 Rui She, Qiyu Kang, Sijie Wang, Yuan-Rui Yang, Kai Zhao, Yang song, Wee Peng Tay

For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing.

Autonomous Vehicles Patch Matching

Comments on "Graphon Signal Processing''

no code implementations23 Oct 2023 Xingchao Jian, Feng Ji, Wee Peng Tay

Because of this error, the proofs of Lemma 3, Theorem 1, Theorem 3, Proposition 2, and Theorem 4 in that paper are no longer valid.

LEMMA valid

Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

1 code implementation NeurIPS 2023 Kai Zhao, Qiyu Kang, Yang song, Rui She, Sijie Wang, Wee Peng Tay

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology.

Adversarial Robustness

Frequency Convergence of Complexon Shift Operators (Extended Version)

no code implementations12 Sep 2023 Purui Zhang, Xingchao Jian, Feng Ji, Wee Peng Tay, Bihan Wen

We recall the notion of a complexon as the limit of a simplicial complex sequence.

Kernel Based Reconstruction for Generalized Graph Signal Processing

no code implementations14 Aug 2023 Xingchao Jian, Wee Peng Tay, Yonina C. Eldar

In this paper, we study GGSP signal reconstruction as a kernel ridge regression (KRR) problem.

The faces of Convolution: from the Fourier theory to algebraic signal processing

no code implementations16 Jul 2023 Feng Ji, Wee Peng Tay, Antonio Ortega

In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of algebraic signal processing.

Approximate Maximum-Likelihood RIS-Aided Positioning

no code implementations13 Jun 2023 Wei zhang, Zhenni Wang, Wee Peng Tay

In this paper, we develop a RIS-aided positioning framework to locate a UE in environments where the LOS path may or may not be available.

Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks

1 code implementation30 May 2023 Qiyu Kang, Kai Zhao, Yang song, Sijie Wang, Wee Peng Tay

In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types.

Graph Embedding Link Prediction +1

Graph Neural Convection-Diffusion with Heterophily

1 code implementation26 May 2023 Kai Zhao, Qiyu Kang, Yang song, Rui She, Sijie Wang, Wee Peng Tay

Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic graphs.

Graph Learning Node Classification

Generalized signals on simplicial complexes

no code implementations11 May 2023 Feng Ji, Xingchao Jian, Wee Peng Tay, Maosheng Yang

Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars.

Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks

1 code implementation29 Apr 2023 Feng Ji, See Hian Lee, Hanyang Meng, Kai Zhao, Jielong Yang, Wee Peng Tay

We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution.

Node Classification

Distributional Signals for Node Classification in Graph Neural Networks

no code implementations7 Apr 2023 Feng Ji, See Hian Lee, Kai Zhao, Wee Peng Tay, Jielong Yang

In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP).

Classification Node Classification

Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks

no code implementations2 Mar 2023 Qiyu Kang, Kai Zhao, Yang song, Sijie Wang, Rui She, Wee Peng Tay

Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data.

Graph signal processing with categorical perspective

no code implementations24 Feb 2023 Feng Ji, Xingchao Jian, Wee Peng Tay

In this paper, we propose a framework for graph signal processing using category theory.

On distributional graph signals

no code implementations22 Feb 2023 Feng Ji, Xingchao Jian, Wee Peng Tay

We develop signal processing tools to study the new notion of distributional graph signals.

RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments

1 code implementation21 Nov 2022 Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay, Andreas Hartmannsgruber, Diego Navarro Navarro

Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments.

Autonomous Driving camera absolute pose regression +2

On semi shift invariant graph filters

no code implementations28 Sep 2022 Feng Ji, See Hian Lee, Wee Peng Tay

In graph signal processing, one of the most important subjects is the study of filters, i. e., linear transformations that capture relations between graph signals.

On the Robustness of Graph Neural Diffusion to Topology Perturbations

1 code implementation16 Sep 2022 Yang song, Qiyu Kang, Sijie Wang, Zhao Kai, Wee Peng Tay

In this work, we explore the robustness properties of graph neural PDEs.

SGAT: Simplicial Graph Attention Network

1 code implementation24 Jul 2022 See Hian Lee, Feng Ji, Wee Peng Tay

In this paper, we present Simplicial Graph Attention Network (SGAT), a simplicial complex approach to represent such high-order interactions by placing features from non-target nodes on the simplices.

Graph Attention Graph Learning +1

Abstract message passing and distributed graph signal processing

no code implementations9 Jun 2022 Feng Ji, Yiqi Lu, Wee Peng Tay, Edwin Chong

Graph signal processing is a framework to handle graph structured data.

Building Facade Parsing R-CNN

1 code implementation12 May 2022 Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay, Diego Navarro Navarro, Andreas Hartmannsgruber

Building facade parsing, which predicts pixel-level labels for building facades, has applications in computer vision perception for autonomous vehicle (AV) driving.

To further understand graph signals

no code implementations2 Mar 2022 Feng Ji, Wee Peng Tay

Graph signal processing (GSP) is a framework to analyze and process graph-structured data.

Wide-Sense Stationarity in Generalized Graph Signal Processing

no code implementations2 Dec 2021 Xingchao Jian, Wee Peng Tay

We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space.

Denoising

Using Reconfigurable Intelligent Surfaces for UE Positioning in mmWave MIMO Systems

no code implementations1 Dec 2021 Wei zhang, Wee Peng Tay

We develop a RIS-aided positioning framework to locate a UE in environments where the LOS path may or may not be available.

Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks

2 code implementations NeurIPS 2021 Qiyu Kang, Yang song, Qinxu Ding, Wee Peng Tay

By ensuring that the equilibrium points of the ODE solution used as part of SODEF is Lyapunov-stable, the ODE solution for an input with a small perturbation converges to the same solution as the unperturbed input.

Signal processing with a distribution of graph operators

no code implementations11 Dec 2020 Feng Ji, Wee Peng Tay

In this paper, we develop a signal processing framework of a network without explicit knowledge of the network topology.

Data-driven Regularized Inference Privacy

no code implementations10 Oct 2020 Chong Xiao Wang, Wee Peng Tay

We develop an inference privacy framework based on the variational method and include maximum mean discrepancy and domain adaption as techniques to regularize the domain of the sanitized data to ensure its legacy compatibility.

Decision Making Domain Adaptation +1

Attentive Graph Neural Networks for Few-Shot Learning

no code implementations14 Jul 2020 Hao Cheng, Joey Tianyi Zhou, Wee Peng Tay, Bihan Wen

Graph Neural Networks (GNN) has demonstrated the superior performance in many challenging applications, including the few-shot learning tasks.

Few-Shot Learning

Subgraph Signal Processing

no code implementations11 May 2020 Feng Ji, Wee Peng Tay, Giacomo Kahn

Graph signal processing, like the graph Fourier transform, requires the full graph signal at every vertex of the graph.

Signal processing on simplicial complexes

no code implementations6 Apr 2020 Feng Ji, Giacomo Kahn, Wee Peng Tay

In this paper, we develop a signal processing framework on simplicial complexes, such that we recover the traditional GSP theory when restricted to signals on graphs.

Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks

no code implementations30 Nov 2019 Yang Song, Qiyu Kang, Wee Peng Tay

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications.

Multi-class Classification

Learning Orthogonal Projections in Linear Bandits

no code implementations26 Jun 2019 Qiyu Kang, Wee Peng Tay

In the case where each arm is chosen from an infinite compact set, our strategy achieves $O(n^{2/3}(\log{n})^{1/2})$ regret.

An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks

1 code implementation25 Jun 2019 Jielong Yang, Wee Peng Tay

An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations.

Variational Inference

GFCN: A New Graph Convolutional Network Based on Parallel Flows

no code implementations25 Feb 2019 Feng Ji, Jielong Yang, Qiang Zhang, Wee Peng Tay

In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data.

General Classification Image Classification +2

Task Recommendation in Crowdsourcing Based on Learning Preferences and Reliabilities

no code implementations27 Jul 2018 Qiyu Kang, Wee Peng Tay

We develop three task recommendation strategies to determine the number of gold tasks for different task categories, and show that they are order optimal.

Using Social Network Information in Bayesian Truth Discovery

no code implementations8 Jun 2018 Jielong Yang, Junshan Wang, Wee Peng Tay

We incorporate knowledge of the agents' social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents' reliabilities, communities, and the event states.

Variational Inference

Sequential Multi-Class Labeling in Crowdsourcing

no code implementations6 Nov 2017 Qiyu Kang, Wee Peng Tay

As workers may be unreliable, we propose to perform sequential questioning in which the questions posed to the workers are designed based on previous questions and answers.

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