Search Results for author: Siheng Chen

Found 77 papers, 31 papers with code

Hypergraph Structure Inference From Data Under Smoothness Prior

no code implementations27 Aug 2023 Bohan Tang, Siheng Chen, Xiaowen Dong

However, existing methods either adopt simple pre-defined rules that fail to precisely capture the distribution of the potential hypergraph structure, or learn a mapping between hypergraph structures and node features but require a large amount of labelled data, i. e., pre-existing hypergraph structures, for training.

Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction

1 code implementation17 Aug 2023 Chenxin Xu, Robby T. Tan, Yuhong Tan, Siheng Chen, Xinchao Wang, Yanfeng Wang

To work with auxiliary tasks, we propose a novel auxiliary-adapted transformer, which can handle incomplete, corrupted motion data and achieve coordinate recovery via capturing spatial-temporal dependencies.

Human motion prediction motion prediction

Joint-Relation Transformer for Multi-Person Motion Prediction

1 code implementation9 Aug 2023 Qingyao Xu, Weibo Mao, Jingze Gong, Chenxin Xu, Siheng Chen, Weidi Xie, Ya zhang, Yanfeng Wang

Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people.

motion prediction

FedDisco: Federated Learning with Discrepancy-Aware Collaboration

1 code implementation30 May 2023 Rui Ye, Mingkai Xu, Jianyu Wang, Chenxin Xu, Siheng Chen, Yanfeng Wang

However, based on our empirical observations and theoretical analysis, we find that the dataset size is not optimal and the discrepancy between local and global category distributions could be a beneficial and complementary indicator for determining aggregation weights.

Federated Learning

Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving

no code implementations24 Apr 2023 Shunli Ren, Zixing Lei, Zi Wang, Mehrdad Dianati, Yafei Wang, Siheng Chen, Wenjun Zhang

To achieve comprehensive recovery, we design a communication adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information.

Autonomous Driving Knowledge Distillation

Collaboration Helps Camera Overtake LiDAR in 3D Detection

1 code implementation CVPR 2023 Yue Hu, Yifan Lu, Runsheng Xu, Weidi Xie, Siheng Chen, Yanfeng Wang

Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems.

Depth Estimation

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

Leapfrog Diffusion Model for Stochastic Trajectory Prediction

1 code implementation CVPR 2023 Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen, Yanfeng Wang

The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed.

Denoising Trajectory Prediction

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving

no code implementations CVPR 2023 Shaoheng Fang, Zi Wang, Yiqi Zhong, Junhao Ge, Siheng Chen, Yanfeng Wang

Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial-temporal priors.

Autonomous Driving

Among Us: Adversarially Robust Collaborative Perception by Consensus

1 code implementation16 Mar 2023 Yiming Li, Qi Fang, Jiamu Bai, Siheng Chen, Felix Juefei-Xu, Chen Feng

This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus.

3D Object Detection Adversarial Defense +2

Robust Collaborative 3D Object Detection in Presence of Pose Errors

1 code implementation14 Nov 2022 Yifan Lu, Quanhao Li, Baoan Liu, Mehrdad Dianati, Chen Feng, Siheng Chen, Yanfeng Wang

Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion.

3D Object Detection object-detection +1

Learning Hypergraphs From Signals With Dual Smoothness Prior

no code implementations3 Nov 2022 Bohan Tang, Siheng Chen, Xiaowen Dong

Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes crucial when a hypergraph topology is not readily available in the datasets.

Unrolled Graph Learning for Multi-Agent Collaboration

no code implementations31 Oct 2022 Enpei Zhang, Shuo Tang, Xiaowen Dong, Siheng Chen, Yanfeng Wang

To fill this gap, we propose a distributed multi-agent learning model inspired by human collaboration, in which the agents can autonomously detect suitable collaborators and refer to collaborators' model for better performance.

Graph Learning Rolling Shutter Correction

Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation

no code implementations18 Oct 2022 Yangheng Zhao, Jun Wang, Xiaolong Li, Yue Hu, Ce Zhang, Yanfeng Wang, Siheng Chen

Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically describe the different point patterns within a semantic class.

3D Semantic Segmentation Scene Understanding

FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning

no code implementations14 Oct 2022 Rui Ye, Zhenyang Ni, Chenxin Xu, Jianyu Wang, Siheng Chen, Yonina C. Eldar

This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space.

Federated Learning

Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps

3 code implementations26 Sep 2022 Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen

Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication.

Monocular 3D Object Detection object-detection

Collaborative Perception for Autonomous Driving: Current Status and Future Trend

no code implementations22 Aug 2022 Shunli Ren, Siheng Chen, Wenjun Zhang

Perception is one of the crucial module of the autonomous driving system, which has made great progress recently.

Autonomous Driving

Aerial Monocular 3D Object Detection

no code implementations8 Aug 2022 Yue Hu, Shaoheng Fang, Weidi Xie, Siheng Chen

To fill the gap, this work proposes a dual-view detection system named DVDET to achieve aerial monocular object detection in both the 2D image space and the 3D physical space.

Autonomous Driving Monocular 3D Object Detection +1

Neural Message Passing for Visual Relationship Detection

1 code implementation8 Aug 2022 Yue Hu, Siheng Chen, Xu Chen, Ya zhang, Xiao Gu

Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions.

Relationship Detection Visual Relationship Detection

Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction

1 code implementation31 Jul 2022 Maosen Li, Siheng Chen, Zijing Zhang, Lingxi Xie, Qi Tian, Ya zhang

To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands.

Human motion prediction motion prediction

Aware of the History: Trajectory Forecasting with the Local Behavior Data

no code implementations20 Jul 2022 Yiqi Zhong, Zhenyang Ni, Siheng Chen, Ulrich Neumann

In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories.

Knowledge Distillation Trajectory Forecasting

Latency-Aware Collaborative Perception

1 code implementation18 Jul 2022 Zixing Lei, Shunli Ren, Yue Hu, Wenjun Zhang, Siheng Chen

Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception.

Autonomous Driving

Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

no code implementations11 Jul 2022 Bohan Tang, Yiqi Zhong, Chenxin Xu, Wei-Tao Wu, Ulrich Neumann, Yanfeng Wang, Ya zhang, Siheng Chen

Further, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty.

regression Trajectory Forecasting

Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning

1 code implementation27 Jun 2022 Chenxin Xu, Yuxi Wei, Bohan Tang, Sheng Yin, Ya zhang, Siheng Chen

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction.

Relational Reasoning Trajectory Prediction

Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling

no code implementations31 May 2022 Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen, Junni Zou, Pascal Frossard, Hongkai Xiong

Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).

GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning

1 code implementation CVPR 2022 Chenxin Xu, Maosen Li, Zhenyang Ni, Ya zhang, Siheng Chen

From the aspect of interaction capturing, we propose a trainable multiscale hypergraph to capture both pair-wise and group-wise interactions at multiple group sizes.

Relational Reasoning Representation Learning +1

Remember Intentions: Retrospective-Memory-based Trajectory Prediction

2 code implementations CVPR 2022 Chenxin Xu, Weibo Mao, Wenjun Zhang, Siheng Chen

However, in this way, the model parameters come from all seen instances, which means a huge amount of irrelevant seen instances might also involve in predicting the current situation, disturbing the performance.

Trajectory Prediction

V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

no code implementations17 Feb 2022 Yiming Li, Dekun Ma, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, Chen Feng

Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving.

Autonomous Driving

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

1 code implementation17 Feb 2022 Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

Multivariate Time Series Forecasting Time Series +1

Task Decoupled Framework for Reference-Based Super-Resolution

no code implementations CVPR 2022 Yixuan Huang, Xiaoyun Zhang, Yu Fu, Siheng Chen, Ya zhang, Yan-Feng Wang, Dazhi He

Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.

Image Super-Resolution Reference-based Super-Resolution

No-Reference Point Cloud Quality Assessment via Domain Adaptation

1 code implementation CVPR 2022 Qi Yang, Yipeng Liu, Siheng Chen, Yiling Xu, Jun Sun

We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds.

Domain Adaptation Point Cloud Quality Assessment

Learning Distilled Collaboration Graph for Multi-Agent Perception

2 code implementations NeurIPS 2021 Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, Wenjun Zhang

Our approach is validated on V2X-Sim 1. 0, a large-scale multi-agent perception dataset that we synthesized using CARLA and SUMO co-simulation.

3D Object Detection Knowledge Distillation +1

Dynamic Differential-Privacy Preserving SGD

no code implementations30 Oct 2021 Jian Du, Song Li, Xiangyi Chen, Siheng Chen, Mingyi Hong

The equivalent privacy costs controlled by maintaining the same gradient clipping thresholds and noise powers in each step result in unstable updates and a lower model accuracy when compared to the non-DP counterpart.

Federated Learning Image Classification +1

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

no code implementations NeurIPS 2021 Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya zhang, Siheng Chen

2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.

Trajectory Forecasting

Spatio-Temporal Graph Complementary Scattering Networks

no code implementations23 Oct 2021 Zida Cheng, Siheng Chen, Ya zhang

Spatio-temporal graph signal analysis has a significant impact on a wide range of applications, including hand/body pose action recognition.

Action Recognition

Learning to Learn Graph Topologies

1 code implementation NeurIPS 2021 Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen

The model is trained in an end-to-end fashion with pairs of node data and graph samples.

Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph

1 code implementation16 Oct 2021 Xin Yu, Jeroen van Baar, Siheng Chen

We use a coarse graph, derived from a dense graph, to estimate the human's 3D pose, and the dense graph to estimate the 3D shape.

3D Human Pose Estimation

A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer

no code implementations24 Sep 2021 Jinxiang Liu, Yangheng Zhao, Siheng Chen, Ya zhang

To leverage the human body shape prior, LPNet exploits the topological information of the body mesh to learn an expressive visual representation for the target person in the 3D mesh space.

Image-to-Image Translation Pose Transfer +1

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction

no code implementations25 Aug 2021 Maosen Li, Siheng Chen, Yangheng Zhao, Ya zhang, Yanfeng Wang, Qi Tian

The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales.

motion prediction

CaT: Weakly Supervised Object Detection with Category Transfer

no code implementations ICCV 2021 Tianyue Cao, Lianyu Du, Xiaoyun Zhang, Siheng Chen, Ya zhang, Yan-Feng Wang

To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets.

object-detection Transfer Learning +1

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network

no code implementations2 Jul 2021 Maosen Li, Siheng Chen, Yanning Shen, Genjia Liu, Ivor W. Tsang, Ya zhang

This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system.

Human motion prediction motion prediction

Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization

no code implementations6 Apr 2021 Chen Ju, Peisen Zhao, Siheng Chen, Ya zhang, Xiaoyun Zhang, Qi Tian

To solve this issue, we introduce an adaptive mutual supervision framework (AMS) with two branches, where the base branch adopts CAS to localize the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through a novel adaptive sampler.

Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1

MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy

1 code implementation4 Mar 2021 Qi Yang, Yujie Zhang, Siheng Chen, Yiling Xu, Jun Sun, Zhan Ma

In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).

Point cloud reconstruction

Divide and Conquer for Single-Frame Temporal Action Localization

no code implementations ICCV 2021 Chen Ju, Peisen Zhao, Siheng Chen, Ya zhang, Yanfeng Wang, Qi Tian

Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.

Temporal Action Localization

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

1 code implementation17 Dec 2020 Chenxin Xu, Siheng Chen, Maosen Li, Ya zhang

To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network.

3D Human Pose Estimation Knowledge Distillation +1

Spatio-Temporal Graph Scattering Transform

no code implementations ICLR 2021 Chao Pan, Siheng Chen, Antonio Ortega

Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.

Time Series Time Series Analysis

Learning on Attribute-Missing Graphs

3 code implementations3 Nov 2020 Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya zhang, Ivor W Tsang

Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.

Graph Learning Link Prediction

Sampling and Recovery of Graph Signals based on Graph Neural Networks

no code implementations3 Nov 2020 Siheng Chen, Maosen Li, Ya zhang

Compared to previous analytical sampling and recovery, the proposed methods are able to flexibly learn a variety of graph signal models from data by leveraging the learning ability of neural networks; compared to previous neural-network-based sampling and recovery, the proposed methods are designed through exploiting specific graph properties and provide interpretability.

Graph Classification Rolling Shutter Correction

Graph Cross Networks with Vertex Infomax Pooling

2 code implementations NeurIPS 2020 Maosen Li, Siheng Chen, Ya zhang, Ivor W. Tsang

Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow.

General Classification Graph Classification

Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

no code implementations16 Jul 2020 Jingchao Su, Xu Chen, Ya zhang, Siheng Chen, Dan Lv, Chenyang Li

The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs.

Relational Reasoning Transfer Learning

Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks

no code implementations17 Jun 2020 Takuya Fujihashi, Toshiaki Koike-Akino, Siheng Chen, Takashi Watanabe

To prevent the cliff effect subject to channel quality fluctuation, we have proposed soft point cloud delivery called HoloCast.

3D Reconstruction Video Compression

Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising

no code implementations1 Jun 2020 Siheng Chen, Yonina C. Eldar, Lingxiao Zhao

We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals.

Denoising Rolling Shutter Correction

Pruned Graph Scattering Transforms

no code implementations ICLR 2020 Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks.

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

1 code implementation17 Mar 2020 Maosen Li, Siheng Chen, Yangheng Zhao, Ya zhang, Yan-Feng Wang, Qi Tian

The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.

3D Human Pose Estimation 3D Pose Estimation +2

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

2 code implementations CVPR 2020 Pengxiang Wu, Siheng Chen, Dimitris Metaxas

The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.

3D Object Detection Autonomous Driving +2

Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring

no code implementations6 Feb 2020 Jingxiao Liu, Bingqing Chen, Siheng Chen, Mario Berges, Jacobo Bielak, HaeYoung Noh

We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.

Efficient and Stable Graph Scattering Transforms via Pruning

no code implementations27 Jan 2020 Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs.

3D Point Cloud Classification Graph Learning +1

Node Attribute Generation on Graphs

3 code implementations23 Jul 2019 Xu Chen, Siheng Chen, Huangjie Zheng, Jiangchao Yao, Kenan Cui, Ya zhang, Ivor W. Tsang

NANG learns a unifying latent representation which is shared by both node attributes and graph structures and can be translated to different modalities.

Data Augmentation General Classification +2

Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving

no code implementations26 Jun 2019 Siheng Chen, Sufeng. Niu, Tian Lan, Baoan Liu

We present a novel graph-neural-network-based system to effectively represent large-scale 3D point clouds with the applications to autonomous driving.

Autonomous Driving Self-Driving Cars

Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

no code implementations11 May 2019 Siheng Chen, Chaojing Duan, Yaoqing Yang, Duanshun Li, Chen Feng, Dong Tian

The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; and (3) graph filtering refines the reconstruction, leading to better performances.

3D Point Cloud Reconstruction General Classification +1

Handwritten Chinese Font Generation with Collaborative Stroke Refinement

no code implementations30 Apr 2019 Chuan Wen, Jie Chang, Ya zhang, Siheng Chen, Yan-Feng Wang, Mei Han, Qi Tian

Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters.

Font Generation

3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation

no code implementations9 Apr 2019 Chaojing Duan, Siheng Chen, Jelena Kovacevic

NPD algorithm uses a neural network to estimate reference planes for points in noisy point clouds.


Generalized Value Iteration Networks: Life Beyond Lattices

1 code implementation8 Jun 2017 Sufeng. Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa C. Smith, Jelena Kovačević

GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs.


Fast Resampling of 3D Point Clouds via Graphs

no code implementations11 Feb 2017 Siheng Chen, Dong Tian, Chen Feng, Anthony Vetro, Jelena Kovačević

We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.

Signal Representations on Graphs: Tools and Applications

no code implementations16 Dec 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.

A statistical perspective of sampling scores for linear regression

no code implementations21 Jul 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

In this paper, we consider a statistical problem of learning a linear model from noisy samples.


Signal Recovery on Graphs: Random versus Experimentally Designed Sampling

no code implementations21 Apr 2015 Siheng Chen, Rohan Varma, Aarti Singh, Jelena Kovačević

We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.

Signal Recovery on Graphs: Variation Minimization

no code implementations26 Nov 2014 Siheng Chen, Aliaksei Sandryhaila, José M. F. Moura, Jelena Kovačević

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.

Anomaly Detection General Classification +2

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