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A Lightweight Neural Model for Biomedical Entity Linking
Biomedical entity linking aims to map biomedical mentions, such as disea...
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Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
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Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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Fine-grain atlases of functional modes for fMRI analysis
Population imaging markedly increased the size of functional-imaging dat...
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NeuroQuery: comprehensive meta-analysis of human brain mapping
Reaching a global view of brain organization requires assembling evidenc...
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Linear predictor on linearly-generated data with missing values: non consistency and solutions
We consider building predictors when the data have missing values. We st...
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Encoding high-cardinality string categorical variables
Statistical analysis usually requires a vector representation of categor...
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Manifold-regression to predict from MEG/EEG brain signals without source modeling
Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
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On the consistency of supervised learning with missing values
In many application settings, the data are plagued with missing features...
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Computational and informatics advances for reproducible data analysis in neuroimaging
The reproducibility of scientific research has become a point of critica...
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Approximate message-passing for convex optimization with non-separable penalties
We introduce an iterative optimization scheme for convex objectives cons...
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Extracting Universal Representations of Cognition across Brain-Imaging Studies
The size of publicly available data in cognitive neuro-imaging has incre...
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Using Feature Grouping as a Stochastic Regularizer for High-Dimensional Noisy Data
The use of complex models --with many parameters-- is challenging with h...
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Text to brain: predicting the spatial distribution of neuroimaging observations from text reports
Despite the digital nature of magnetic resonance imaging, the resulting ...
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Similarity encoding for learning with dirty categorical variables
For statistical learning, categorical variables in a table are usually c...
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Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public br...
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Cross-validation failure: small sample sizes lead to large error bars
Predictive models ground many state-of-the-art developments in statistic...
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Stochastic Subsampling for Factorizing Huge Matrices
We present a matrix-factorization algorithm that scales to input matrice...
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Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the p...
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Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals
-In this work, we revisit fast dimension reduction approaches, as with r...
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Social-sparsity brain decoders: faster spatial sparsity
Spatially-sparse predictors are good models for brain decoding: they giv...
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Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Decoding, ie prediction from brain images or signals, calls for empirica...
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Learning to Discover Sparse Graphical Models
We consider structure discovery of undirected graphical models from obse...
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Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable da...
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Compressed Online Dictionary Learning for Fast fMRI Decomposition
We present a method for fast resting-state fMRI spatial decomposi-tions ...
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Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Functional brain networks are well described and estimated from data wit...
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FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging
The total variation (TV) penalty, as many other analysis-sparsity proble...
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Fast clustering for scalable statistical analysis on structured images
The use of brain images as markers for diseases or behavioral difference...
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Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Functional Magnetic Resonance Images acquired during resting-state provi...
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Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimag...
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Mapping cognitive ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cogniti...
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PyXNAT: XNAT in Python
As neuroimaging databases grow in size and complexity, the time research...
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Improving accuracy and power with transfer learning using a meta-analytic database
Typical cohorts in brain imaging studies are not large enough for system...
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On spatial selectivity and prediction across conditions with fMRI
Researchers in functional neuroimaging mostly use activation coordinates...
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Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the se...
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Improved brain pattern recovery through ranking approaches
Inferring the functional specificity of brain regions from functional Ma...
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Small-sample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering
Functional neuroimaging can measure the brain?s response to an external ...
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A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observ...
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Total variation regularization for fMRI-based prediction of behaviour
While medical imaging typically provides massive amounts of data, the ex...
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Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has ...
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CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Spatial Independent Component Analysis (ICA) is an increasingly used dat...
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