no code implementations • 29 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.
no code implementations • 25 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.
no code implementations • 3 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.
no code implementations • 11 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.
no code implementations • ICCV 2023 • Enze Ye, Yuhang Wang, Hong Zhang, Yiqin Gao, Huan Wang, He Sun
To our knowledge, our work is the first attempt to directly recover 3D structures of a temporally-varying particle from liquid-phase EM movies.
Cryogenic Electron Microscopy (cryo-EM)
Object Reconstruction
+1
1 code implementation • 16 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.
no code implementations • 12 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.
1 code implementation • 5 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.
no code implementations • 21 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.
no code implementations • 8 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.
1 code implementation • 2 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.
no code implementations • 26 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.
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.
no code implementations • 28 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.
Ranked #1 on
Unsupervised Person Re-Identification
on DukeMTMCreID
(MAP metric)
1 code implementation • 21 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.
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.
1 code implementation • 9 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.
1 code implementation • 13 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.
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.
1 code implementation • 27 Oct 2020 • He Sun, Katherine L. Bouman
In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty.
no code implementations • 26 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.
no code implementations • 19 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
2 code implementations • 18 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).
no code implementations • 23 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.
no code implementations • 6 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.
1 code implementation • 15 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.
no code implementations • 3 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
no code implementations • 27 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.
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.
no code implementations • 18 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.
no code implementations • 7 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.