Bilwaj Gaonkar
Cognitive Neuroscience · 2012–2015
A researcher in the Science corpus: 3 papers; trusts 55 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 37 citations into it
- Radiology, Nuclear Medicine and Imaging — 26 citations into it
- Computer Vision and Pattern Recognition — 23 citations into it; #8,624 of 21,575 by trust here, #16,367 by citations
- Artificial Intelligence — 20 citations into it; #18,330 of 35,345 by trust here, #26,682 by citations
- Geometry and Topology — 9 citations into it
- Neurology — 7 citations into it
- Psychiatry and Mental health — 7 citations into it
- Statistics and Probability — 7 citations into it
Whom Bilwaj Gaonkar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christos Davatzikos
- John Ashburner
- Vladimir Naumovich Vapni
- Karl J. Friston
- Stephen M. Smith
- Moo K. Chung
- Ze Wang
- R. Craddock
- Aixia Guo
- Fortunato Pesarin
Who trusts Bilwaj Gaonkar overall
- Ender Konukoglu
- Ben Glocker
- Alexandre Gramfort
- Joset A. Etzel
- Mohammad R. Arbabshirani
- Christian F. Baumgartner
- Eduardo Mosqueira-Rey
- Russell T. Shinohara
- Bertrand Thirion
- Jeffrey M. Zacks
Papers from Bilwaj Gaonkar's perspective
Ordered by how strongly the researchers Bilwaj Gaonkar trusts endorse each paper. Same corpus, a different order for every researcher.
- Voxel-based morphometry--the methods.
- Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data
- The Nature of Statistical Learning Theory
- Pattern Recognition and Machine Learning
- Spatial pattern analysis of functional brain images using partial least squares.
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- Automatic classification of MR scans in Alzheimer’s disease
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Characterizing dynamic brain responses with fMRI: a multivariate approach.
- ImageNet classification with deep convolutional neural networks
Their papers
- Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013) — 123 citations
- Deriving statistical significance maps for SVM based image classification and group comparisons (2012) — 26 citations
- Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015) — 67 citations