Search Results for author: He Sun

Found 37 papers, 11 papers with code

Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method

no code implementations15 Oct 2024 Weimin Bai, Weiheng Tang, Enze Ye, Siyi Chen, Wenzheng Chen, He Sun

Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems.

Deblurring Denoising

Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images

no code implementations15 Jul 2024 Yifei Wang, Weimin Bai, Weijian Luo, Wenzheng Chen, He Sun

The conditional normalizing flow try to learn to recover clean images through a novel amortized inference mechanism, and can thus effectively facilitate the diffusion model's training with corrupted data.

Deblurring Denoising

Blind Inversion using Latent Diffusion Priors

no code implementations1 Jul 2024 Weimin Bai, Siyi Chen, Wenzheng Chen, He Sun

Additionally, many current approaches rely on pixel-space diffusion models, leaving the potential of more powerful latent diffusion models (LDMs) underexplored.

Deblurring Image Restoration +1

An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

no code implementations1 Jul 2024 Weimin Bai, Yifei Wang, Wenzheng Chen, He Sun

Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors.

Deblurring Denoising

Spectral Toolkit of Algorithms for Graphs: Technical Report (2)

no code implementations6 Jun 2024 Peter Macgregor, He Sun

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient graph algorithms.

Clustering Density Estimation

Dynamic Spectral Clustering with Provable Approximation Guarantee

1 code implementation5 Jun 2024 Steinar Laenen, He Sun

This paper studies clustering algorithms for dynamically evolving graphs $\{G_t\}$, in which new edges (and potential new vertices) are added into a graph, and the underlying cluster structure of the graph can gradually change.

Clustering

Reconstructing Satellites in 3D from Amateur Telescope Images

no code implementations29 Apr 2024 Zhiming Chang, Boyang Liu, Yifei Xia, Youming Guo, Boxin Shi, He Sun

This paper proposes a framework for the 3D reconstruction of satellites in low-Earth orbit, utilizing videos captured by small amateur telescopes.

3D Reconstruction Image Restoration +1

Neural Born Series Operator for Biomedical Ultrasound Computed Tomography

no code implementations25 Dec 2023 Zhijun Zeng, Yihang Zheng, Youjia Zheng, Yubing Li, Zuoqiang Shi, He Sun

Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging.

Image Reconstruction

MoEC: Mixture of Experts Implicit Neural Compression

no code implementations3 Dec 2023 Jianchen Zhao, Cheng-Ching Tseng, Ming Lu, Ruichuan An, Xiaobao Wei, He Sun, Shanghang Zhang

However, manually designing the partition scheme for a complex scene is very challenging and fails to jointly learn the partition and INRs.

Data Compression

Revive, Restore, Revitalize: An Eco-economic Methodology for Maasai Mara

no code implementations11 Sep 2023 Yipeng Xu, He Sun, Junfeng Zhu

Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types.

Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs

1 code implementation16 Jun 2023 Steinar Laenen, Bogdan-Adrian Manghiuc, He Sun

This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function.

Clustering

Discovering Structure From Corruption for Unsupervised Image Reconstruction

no code implementations12 Apr 2023 Oscar Leong, Angela F. Gao, He Sun, Katherine L. Bouman

We show that such a set of inverse problems can be solved simultaneously without the use of a spatial image prior by instead inferring a shared image generator with a low-dimensional latent space.

Denoising Image Reconstruction +1

Spectral Toolkit of Algorithms for Graphs: Technical Report (1)

1 code implementation5 Apr 2023 Peter Macgregor, He Sun

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022.

Clustering Graph Clustering

Image Reconstruction without Explicit Priors

no code implementations21 Mar 2023 Angela F. Gao, Oscar Leong, He Sun, Katherine L. Bouman

We show that such a set of inverse problems can be solved simultaneously by learning a shared image generator with a low-dimensional latent space.

Image Reconstruction

Reinforcement Learning with Stepwise Fairness Constraints

no code implementations8 Nov 2022 Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes

AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making.

Decision Making Fairness +3

A Tighter Analysis of Spectral Clustering, and Beyond

1 code implementation2 Aug 2022 Peter Macgregor, He Sun

For the second result, we show that, by applying fewer than $k$ eigenvectors to construct the embedding, spectral clustering is able to produce better output for many practical instances; this result is the first of its kind in spectral clustering.

Clustering

Nondestructive Quality Control in Powder Metallurgy using Hyperspectral Imaging

no code implementations26 Jul 2022 Yijun Yan, Jinchang Ren, He Sun

Measuring the purity in the metal powder is critical for preserving the quality of additive manufacturing products.

Finding Bipartite Components in Hypergraphs

1 code implementation NeurIPS 2021 Peter Macgregor, He Sun

Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice.

Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification

no code implementations28 Jan 2022 He Sun, Mingkun Li, Chun-Guang Li

The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network.

Clustering Clustering Ensemble +2

alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction

1 code implementation21 Jan 2022 He Sun, Katherine L. Bouman, Paul Tiede, Jason J. Wang, Sarah Blunt, Dimitri Mawet

Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations.

Uncertainty Quantification Variational Inference

Hierarchical Clustering: $O(1)$-Approximation for Well-Clustered Graphs

no code implementations NeurIPS 2021 Bogdan-Adrian Manghiuc, He Sun

Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis.

Clustering

Local Algorithms for Finding Densely Connected Clusters

1 code implementation9 Jun 2021 Peter Macgregor, He Sun

Local graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science.

Clustering Graph Clustering

End-to-End Sequential Sampling and Reconstruction for MRI

1 code implementation13 May 2021 Tianwei Yin, Zihui Wu, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman

In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy.

Higher-Order Spectral Clustering of Directed Graphs

no code implementations NeurIPS 2020 Steinar Laenen, He Sun

Clustering is an important topic in algorithms, and has a number of applications in machine learning, computer vision, statistics, and several other research disciplines.

Clustering Graph Clustering

Decision-Aware Conditional GANs for Time Series Data

no code implementations26 Sep 2020 He Sun, Zhun Deng, Hui Chen, David C. Parkes

We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.

Generative Adversarial Network Time Series +2

High-Contrast Integral Field Spectrograph (HCIFS): multi-spectral wavefront control and reduced-dimensional system identification

no code implementations19 May 2020 He Sun, Alexei Goun, Susan Redmond, Michael Galvin, Tyler Groff, Maxime Rizzo, N. Jeremy Kasdin

Any high-contrast imaging instrument in a future large space-based telescope will include an integral field spectrograph (IFS) for measuring broadband starlight residuals and characterizing the exoplanet's atmospheric spectrum.

Instrumentation and Methods for Astrophysics Optics

Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery

2 code implementations18 May 2020 Junjun Jiang, He Sun, Xian-Ming Liu, Jiayi Ma

Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs).

Hyperspectral Image Super-Resolution Image Super-Resolution

Learning a Probabilistic Strategy for Computational Imaging Sensor Selection

no code implementations23 Mar 2020 He Sun, Adrian V. Dalca, Katherine L. Bouman

In this paper, we demonstrate the approach in the context of a very-long-baseline-interferometry (VLBI) array design task, where sensor correlations and atmospheric noise present unique challenges.

Hermitian matrices for clustering directed graphs: insights and applications

no code implementations6 Aug 2019 Mihai Cucuringu, Huan Li, He Sun, Luca Zanetti

Graph clustering is a basic technique in machine learning, and has widespread applications in different domains.

Clustering Graph Clustering +1

Temporal Human Action Segmentation via Dynamic Clustering

1 code implementation15 Mar 2018 Yan Zhang, He Sun, Siyu Tang, Heiko Neumann

We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring.

Action Segmentation Clustering

Distributed Graph Clustering and Sparsification

no code implementations3 Nov 2017 He Sun, Luca Zanetti

Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

An SDP-Based Algorithm for Linear-Sized Spectral Sparsification

no code implementations27 Feb 2017 Yin Tat Lee, He Sun

Noticing that $\Omega(m)$ time is needed for any algorithm to construct a spectral sparsifier and a spectral sparsifier of $G$ requires $\Omega(n)$ edges, a natural question is to investigate, for any constant $\varepsilon$, if a $(1+\varepsilon)$-spectral sparsifier of $G$ with $O(n)$ edges can be constructed in $\tilde{O}(m)$ time, where the $\tilde{O}$ notation suppresses polylogarithmic factors.

Communication-Optimal Distributed Clustering

no code implementations NeurIPS 2016 Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang

We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site.

Clustering

Distributed Graph Clustering by Load Balancing

no code implementations18 Jul 2016 He Sun, Luca Zanetti

In this paper we present a simple and distributed algorithm for graph clustering: for a wide class of graphs that are characterised by a strong cluster-structure, our algorithm finishes in a poly-logarithmic number of rounds, and recovers a partition of the graph close to an optimal partition.

Clustering Distributed Computing +1

Partitioning Well-Clustered Graphs: Spectral Clustering Works!

no code implementations7 Nov 2014 Richard Peng, He Sun, Luca Zanetti

In this paper we study variants of the widely used spectral clustering that partitions a graph into k clusters by (1) embedding the vertices of a graph into a low-dimensional space using the bottom eigenvectors of the Laplacian matrix, and (2) grouping the embedded points into k clusters via k-means algorithms.

Clustering

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