Jacquelyn A Shelton
Cited by
Cited by
Feature selection via dependence maximization
L Song, A Smola, A Gretton, J Bedo, K Borgwardt
Journal of Machine Learning Research 13 (May), 1393-1434, 2012
Semi-supervised kernel canonical correlation analysis with application to human fMRI
MB Blaschko, JA Shelton, A Bartels, CH Lampert, A Gretton
Pattern Recognition Letters 32 (11), 1572-1583, 2011
A truncated EM approach for spike-and-slab sparse coding
AS Sheikh, JA Shelton, J Lücke
The Journal of Machine Learning Research 15 (1), 2653-2687, 2014
Select and sample-a model of efficient neural inference and learning
JA Shelton, AS Sheikh, P Berkes, J Bornschein, J Lücke
Advances in neural information processing systems, 2618-2626, 2011
GP-select: Accelerating EM using adaptive subspace preselection
JA Shelton, J Gasthaus, Z Dai, J Lücke, A Gretton
Neural Computation 29 (8), 2177-2202, 2017
Nonlinear spike-and-slab sparse coding for interpretable image encoding
JA Shelton, AS Sheikh, J Bornschein, P Sterne, J Luecke
PLoS One 10 (5), 2015
Augmenting feature-driven fMRI analyses: Semi-supervised learning and resting state activity
A Bartels, M Blaschko, JA Shelton
Advances in Neural Information Processing Systems, 126-134, 2009
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding
P Sterne, J Bornschein, A Sheikh, J Lücke, JA Shelton
Advances in neural information processing systems, 2276-2284, 2012
Challenges of developing engineering students’ writing through peer assessment
T McConlogue, J Mueller, J Shelton
The Higher Education Academy Engineering Subject Centre, EE, 2010
Semi-supervised subspace analysis of human functional magnetic resonance imaging data
J Shelton, M Blaschko, A Bartels
Max Planck Institute for Biological Cybernetics, 2009
STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds
AS Sheikh, NS Harper, J Drefs, Y Singer, Z Dai, RE Turner, J Lücke
PLoS computational biology 15 (1), e1006595, 2019
Similarities in resting state and feature-driven activity: Non-parametric evaluation of human fMRI
JA Shelton, MB Blaschko, A Gretton, J Müller, E Fischer, A Bartels
NIPS 2010 Workshop on Learning and Planning from Batch Time Series Data, 1-2, 2010
Semi-supervised subspace learning and application to human functional magnetic brain resonance imaging data
J Shelton
Eberhard Karls Universität Tübingen, Germany, 2010
Large-scale approximate EM-style learning and inference in generative graphical models for sparse coding
JA Shelton
Technical University of Berlin, Germany, 2018
Augmentation of fMRI data analysis using resting state activity and Semi-supervised Canonical Correlation Analysis
JA Shelton, MB Blaschko, A Bartels
NIPS 2010 Women in Machine Learning Workshop (WiML 2010), 2010
Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data
JA Shelton
Women in Machine Learning Workshop (WiML 2009), 2009
Semi-supervised Analysis of Human fMRI Data
JA Shelton, MB Blaschko, CH Lampert, A Bartels
Berlin BCI Workshop 2009: Advances in Nanotechnology, 2009
Semi-supervised Analysis of Human fMRI
JA Shelton, M Blaschko, CH Lampert, A Bartels
Berlin Brain-Computer Interface Workshop (BBCI), 2009
Augmenting Feature-driven fMRI Analyses: Semi-supervised Learning and Resting State Activity
JA Shelton, MB Blaschko, A Bartels
Combining approximate inference methods for efficient learning on large computer clusters
Z Dai, JA Shelton, J Bornschein, AS Sheikh, J Lücke
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