D
Demba Ba
Researcher at Harvard University
Publications - 90
Citations - 1517
Demba Ba is an academic researcher from Harvard University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 19, co-authored 80 publications receiving 1263 citations. Previous affiliations of Demba Ba include University of Jinan & Picower Institute for Learning and Memory.
Papers
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Journal ArticleDOI
Maximum Likelihood Sound Source Localization and Beamforming for Directional Microphone Arrays in Distributed Meetings
TL;DR: A unified maximum likelihood framework of these two techniques is presented, and it is demonstrated how such a framework can be adapted to create efficient SSL and beamforming algorithms for reverberant rooms and unknown directional patterns of microphones.
Journal ArticleDOI
Corticoamygdala Transfer of Socially Derived Information Gates Observational Learning
Stephen A. Allsop,Romy Wichmann,Fergil Mills,Anthony Burgos-Robles,Chia-Jung Chang,Ada C. Felix-Ortiz,Alienor Vienne,Anna Beyeler,Ehsan M. Izadmehr,Gordon F. Glober,Meghan I. Cum,Johanna Stergiadou,Kavitha K. Anandalingam,Kathryn Farris,Praneeth Namburi,Christopher A. Leppla,Javier C. Weddington,Edward H. Nieh,Anne C. Smith,Demba Ba,Emery N. Brown,Kay M. Tye +21 more
TL;DR: It is shown that information derived from observation about the aversive value of the cue is transmitted from the ACC to the BLA and that this routing of information is critically instructive for observational fear conditioning.
Journal ArticleDOI
A Shared Vision for Machine Learning in Neuroscience
Mai-Anh T. Vu,Tulay Adali,Demba Ba,György Buzsáki,David E. Carlson,Katherine Heller,Conor Liston,Cynthia Rudin,Vikaas S. Sohal,Alik S. Widge,Helen S. Mayberg,Guillermo Sapiro,Kafui Dzirasa +12 more
TL;DR: At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.
Journal ArticleDOI
Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms
TL;DR: A one-to-one correspondence between the IRLS algorithms and a class of Expectation-Maximization algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribution is demonstrated.
Patent
System and method for estimating high time-frequency resolution eeg spectrograms to monitor patient state
TL;DR: In this article, a system and method for monitoring a patient includes a sensor configured to acquire physiological data from a patient and a processor configured to receive the data from at least one sensor.