M
Malte J. Rasch
Researcher at IBM
Publications - 68
Citations - 10158
Malte J. Rasch is an academic researcher from IBM. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 22, co-authored 55 publications receiving 7904 citations. Previous affiliations of Malte J. Rasch include McGovern Institute for Brain Research & Boston Children's Hospital.
Papers
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Journal ArticleDOI
A kernel two-sample test
TL;DR: This work proposes a framework for analyzing and comparing distributions, which is used to construct statistical tests to determine if two samples are drawn from different distributions, and presents two distribution free tests based on large deviation bounds for the maximum mean discrepancy (MMD).
Proceedings Article
A Kernel Method for the Two-Sample-Problem
TL;DR: This work proposes two statistical tests to determine if two samples are from different distributions, and applies this approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where the test performs strongly.
Journal ArticleDOI
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Karsten M. Borgwardt,Arthur Gretton,Malte J. Rasch,Hans-Peter Kriegel,Bernhard Schölkopf,Alexander J. Smola +5 more
TL;DR: A novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by the experiments.
Posted Content
A Kernel Method for the Two-Sample Problem
TL;DR: In this paper, the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS) is defined, and the test statistic can be computed in quadratic time, although efficient linear time approximations are available.
Journal ArticleDOI
Phase-of-Firing Coding of Natural Visual Stimuli in Primary Visual Cortex
Marcelo A. Montemurro,Malte J. Rasch,Yusuke Murayama,Nikos K. Logothetis,Nikos K. Logothetis,Stefano Panzeri,Stefano Panzeri +6 more
TL;DR: It is found that at low LFP frequencies, the phase of firing conveyed 54% additional information beyond that conveyed by spike counts, which may allow primary cortical neurons to represent several effective stimuli in an easily decodable format.