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Found 65 papers, 29 papers with code
Learning sources of variability from high-dimensional observational studies
Causal inference studies whether the presence of a variable influences an observed outcome.
Polarity is all you need to learn and transfer faster
Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples.
Preserving Derivative Information while Transforming Neuronal Curves
Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between.
Approximately optimal domain adaptation with Fisher's Linear Discriminant
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.
The Value of Out-of-Distribution Data
In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples.
Deep Discriminative to Kernel Density Graph for In- and Out-of-distribution Calibrated Inference
However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Graph Matching via Optimal Transport
The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements.
Simplest Streaming Trees
In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations.
Towards a theory of out-of-distribution learning
This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.
Federated Causal Inference in Heterogeneous Observational Data
We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site.
Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction
Our most probable estimation method models the task of reconstructing neuronal processes in the presence of other neurons, and thus is applicable in images with several neurons.
Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry
Our representation makes it possible to compute these parameters from neuron traces in closed form.
Discovery of Multi-Level Network Differences Across Populations of Heterogeneous Connectomes
A connectome is a map of the structural and/or functional connections in the brain.
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
Robust Similarity and Distance Learning via Decision Forests
Many algorithms have been proposed for automated learning of suitable distances, most of which employ linear methods to learn a global metric over the feature space.
mvlearn: Multiview Machine Learning in Python
As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.
Distance-based Positive and Unlabeled Learning for Ranking
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
A New Age of Computing and the Brain
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection
Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n=18, 547) and three cohorts with pneumonia (n=400, 907).
The Chi-Square Test of Distance Correlation
One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data.
High-dimensional and universally consistent k-sample tests
The evaluation included several popular independence statistics and covered a comprehensive set of simulations.
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices.
AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python
Background: Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation.
Independence Testing for Temporal Data
While many non-parametric and universally consistent dependence measures have recently been proposed, directly applying them to temporal data can inflate the p-value and result in invalid test.
Graphyti: A Semi-External Memory Graph Library for FlashGraph
Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.
Distributed, Parallel, and Cluster Computing Databases
Geodesic Learning via Unsupervised Decision Forests
Empirical results on simulated and real data demonstrate that URerF is robust to high-dimensional noise, where as other methods, such as Isomap, UMAP, and FLANN, quickly deteriorate in such settings.
hyppo: A Multivariate Hypothesis Testing Python Package
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing.
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.
GraSPy: Graph Statistics in Python
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations.
Learning Interpretable Characteristic Kernels via Decision Forests
Decision forests are widely used for classification and regression tasks.
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community.
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies.
Neurons and Cognition Quantitative Methods
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
Kernel k-Groups via Hartigan's Method
In this paper, we consider a formulation for the clustering problem using a weighted version of energy statistics in spaces of negative type.
From Distance Correlation to Multiscale Graph Correlation
Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age.
Statistical inference on random dot product graphs: a survey
In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.
Supervised Dimensionality Reduction for Big Data
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences.
Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.
Joint Embedding of Graphs
Feature extraction and dimension reduction for networks is critical in a wide variety of domains.
A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases.
Probabilistic Fluorescence-Based Synapse Detection
The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.
Discovering and Deciphering Relationships Across Disparate Data Modalities
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.
Connectome Smoothing via Low-rank Approximations
In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem.
knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library
The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.
Distributed, Parallel, and Cluster Computing
Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM
Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks.
FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs
R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.
Distributed, Parallel, and Cluster Computing
Quantifying mesoscale neuroanatomy using X-ray microtomography
Methods for resolving the 3D microstructure of the brain typically start by thinly slicing and staining the brain, and then imaging each individual section with visible light photons or electrons.
Sparse Projection Oblique Randomer Forests
Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.
Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection
Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space.
An Automated Images-to-Graphs Framework for High Resolution Connectomics
Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set.
Covariate-assisted spectral clustering
We utilize these node covariates to help uncover latent communities in a graph, using a modification of spectral clustering.
Graph Matching: Relax at Your Own Risk
Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes
The contribution of this work is the introduction of a straightforward and robust pipeline which annotates axoplasmic reticula with high precision, contributing towards advancements in automatic feature annotations in neural EM data.
VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale
An open challenge problem at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced understanding of the wiring diagram of the brain promises to lead to new breakthroughs in diagnosing and treating neurological disorders.
Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm.
Real-Time Inference for a Gamma Process Model of Neural Spiking
With simultaneous measurements from ever increasing populations of neurons, there is a growing need for sophisticated tools to recover signals from individual neurons.
Spectral Clustering for Divide-and-Conquer Graph Matching
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.
Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance.
Statistical inference on errorfully observed graphs
Thus we errorfully observe $G$ when we observe the graph $\widetilde{G} = (V,\widetilde{E})$ as the edges in $\widetilde{E}$ arise from the classifications of the "edge-features", and are expected to be errorful.
