Jacquelyn A Shelton
Title
Cited by
Cited by
Year
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
2702012
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
432011
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
352014
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
182011
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
142017
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
142015
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
142009
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
82012
Challenges of developing engineering students’ writing through peer assessment
T McConlogue, J Mueller, J Shelton
The Higher Education Academy Engineering Subject Centre, EE, 2010
52010
Semi-supervised subspace analysis of human functional magnetic resonance imaging data
J Shelton, M Blaschko, A Bartels
Max Planck Institute for Biological Cybernetics, 2009
52009
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
42019
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
32010
Semi-supervised subspace learning and application to human functional magnetic brain resonance imaging data
J Shelton
Eberhard Karls Universität Tübingen, Germany, 2010
22010
Large-scale approximate EM-style learning and inference in generative graphical models for sparse coding
JA Shelton
Technical University of Berlin, Germany, 2018
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
2010
Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data
JA Shelton
Women in Machine Learning Workshop (WiML 2009), 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
2009
Semi-supervised Analysis of Human fMRI
JA Shelton, M Blaschko, CH Lampert, A Bartels
Berlin Brain-Computer Interface Workshop (BBCI), 2009
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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