
mvlearn: Multiview Machine Learning in Python
As data are generated more and more from multiple disparate sources, mul...
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Manifold Forests: Closing the Gap on Neural Networks
Decision forests (DF), in particular random forests and gradient boostin...
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A general approach to progressive intelligence
In biological learning, data is used to improve performance on the task ...
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The ChiSquare Test of Distance Correlation
Distance correlation has gained much recent attention in the statistics ...
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Learning to rank via combining representations
Learning to rank – producing a ranked list of items specific to a query ...
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A New Age of Computing and the Brain
The history of computer science and brain sciences are intertwined. In h...
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On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without an...
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A general approach to progressive learning
In biological learning, data is used to improve performance on the task ...
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Geodesic Learning via Unsupervised Decision Forests
Geodesic distance is the shortest path between two points in a Riemannia...
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From Distance Correlation to Multiscale Generalized Correlation
Understanding and developing a correlation measure that can detect gener...
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Statistical inference on random dot product graphs: a survey
The random dot product graph (RDPG) is an independentedge random graph ...
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Linear Optimal Low Rank Projection for HighDimensional MultiClass Data
Classification of individual samples into one or more categories is crit...
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Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Droso...
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Joint Embedding of Graphs
Feature extraction and dimension reduction for networks is critical in a...
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Discovering Relationships and their Structures Across Disparate Data Modalities
Determining whether certain properties are related to other properties i...
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Law of Large Graphs
Estimating the mean of a population of graphs based on a sample is a cor...
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A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
CLARITY is a method for converting biological tissues into translucent a...
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Probabilistic FluorescenceBased Synapse Detection
Brain function results from communication between neurons connected by c...
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Randomer Forests
Random forests (RF) is a popular general purpose classifier that has bee...
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Quantifying mesoscale neuroanatomy using Xray microtomography
Methods for resolving the 3D microstructure of the brain typically start...
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Manifold Matching using ShortestPath Distance and Joint Neighborhood Selection
Matching datasets of multiple modalities has become an important task in...
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Covariateassisted spectral clustering
Biological and social systems consist of myriad interacting units. The i...
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Graph Matching: Relax at Your Own Risk
Graph matchingaligning a pair of graphs to minimize their edge disagr...
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Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
We present a novel approximate graph matching algorithm that incorporate...
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Spectral Clustering for DivideandConquer Graph Matching
We present a parallelized bijective graph matching algorithm that levera...
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Statistical inference on errorfully observed graphs
Statistical inference on graphs is a burgeoning field in the applied and...
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An Automated ImagestoGraphs Framework for High Resolution Connectomics
Reconstructing a map of neuronal connectivity is a critical challenge in...
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Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes
In this paper, we present a new pipeline which automatically identifies ...
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Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance. Th...
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Energy Clustering
Energy statistics was proposed by Székely in the 80's inspired by the Ne...
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Signal Subgraph Estimation Via Vertex Screening
Graph classification and regression have wide applications in a variety ...
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The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
Distancebased methods, also called "energy statistics", are leading met...
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Decision Forests Induce Characteristic Kernels
Decision forests are popular tools for classification and regression. Th...
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clusterNOR: A NUMAOptimized Clustering Framework
Clustering algorithms are iterative and have complex data access pattern...
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Forest Packing: Fast, Parallel Decision Forests
Machine learning has an emerging critical role in highperformance compu...
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mgcpy: A Comprehensive High Dimensional Independence Testing Python Package
With the increase in the amount of data in many fields, a method to cons...
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Graph Independence Testing
Identifying statistically significant dependency between variables is a ...
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Inference for multiple heterogeneous networks with a common invariant subspace
The development of models for multiple heterogeneous network data is of ...
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AutoGMM: Automatic Gaussian Mixture Modeling in Python
Gaussian mixture modeling is a fundamental tool in clustering, as well a...
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The Exact Equivalence of Independence Testing and TwoSample Testing
Testing independence and testing equality of distributions are two tight...
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Graphyti: A SemiExternal Memory Graph Library for FlashGraph
Graph datasets exceed the inmemory capacity of most standalone machines...
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Improving Power of 2Sample Random Graph Tests with Applications in Connectomics
In many applications, there is an interest in testing whether two graphs...
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hyppo: A Comprehensive Multivariate Hypothesis Testing Python Package
We introduce hyppo, a unified library for performing multivariate hypoth...
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Statistical Analysis of Data Repeatability Measures
The advent of modern data collection and processing techniques has seen ...
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Joshua T. Vogelstein
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Assistant Professor Dept. of Biomedical Engineering at Johns Hopkins University