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Found 30 papers, 10 papers with code
Neural Born Series Operator for Biomedical Ultrasound Computed Tomography
Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging.
MoEC: Mixture of Experts Implicit Neural Compression
However, manually designing the partition scheme for a complex scene is very challenging and fails to jointly learn the partition and INRs.
Revive, Restore, Revitalize: An Eco-economic Methodology for Maasai Mara
Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types.
Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Movies
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
Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs
This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function.
Discovering Structure From Corruption for Unsupervised Image Reconstruction
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.
Spectral Toolkit of Algorithms for Graphs: Technical Report (1)
Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022.
Image Reconstruction without Explicit Priors
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.
Reinforcement Learning with Stepwise Fairness Constraints
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.
A Tighter Analysis of Spectral Clustering, and Beyond
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.
Nondestructive Quality Control in Powder Metallurgy using Hyperspectral Imaging
Measuring the purity in the metal powder is critical for preserving the quality of additive manufacturing products.
Finding Bipartite Components in Hypergraphs
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
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)
alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction
Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations.
Hierarchical Clustering: $O(1)$-Approximation for Well-Clustered Graphs
Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis.
Local Algorithms for Finding Densely Connected Clusters
Local graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science.
End-to-End Sequential Sampling and Reconstruction for MRI
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
Clustering is an important topic in algorithms, and has a number of applications in machine learning, computer vision, statistics, and several other research disciplines.
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging
In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty.
Decision-Aware Conditional GANs for Time Series Data
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.
High-Contrast Integral Field Spectrograph (HCIFS): multi-spectral wavefront control and reduced-dimensional system identification
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
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).
Learning a Probabilistic Strategy for Computational Imaging Sensor Selection
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
Graph clustering is a basic technique in machine learning, and has widespread applications in different domains.
Temporal Human Action Segmentation via Dynamic Clustering
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.
Distributed Graph Clustering and Sparsification
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
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
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.
Distributed Graph Clustering by Load Balancing
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.
Partitioning Well-Clustered Graphs: Spectral Clustering Works!
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.
