S
Subhaneil Lahiri
Researcher at Stanford University
Publications - 20
Citations - 1710
Subhaneil Lahiri is an academic researcher from Stanford University. The author has contributed to research in topics: Space (mathematics) & Yang–Mills theory. The author has an hindex of 15, co-authored 20 publications receiving 1411 citations. Previous affiliations of Subhaneil Lahiri include Harvard University & Tata Institute of Fundamental Research.
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Exponential expressivity in deep neural networks through transient chaos
TL;DR: In this article, the authors combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights.
Proceedings Article
Exponential expressivity in deep neural networks through transient chaos
TL;DR: In this article, the authors combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in deep neural networks with random weights, and reveal a phase transition in the expressivity of random deep networks.
Journal ArticleDOI
Large rotating AdS black holes from fluid mechanics
TL;DR: In this article, the authors use the AdS/CFT correspondence to argue that large rotating black holes in global AdSD spaces are dual to stationary solutions of the relativistic Navier-Stokes equations on SD−2.
Journal ArticleDOI
Supersymmetric states of N=4 Yang-Mills from giant gravitons
Indranil Biswas,Davide Gaiotto,Subhaneil Lahiri,Subhaneil Lahiri,Shiraz Minwalla,Shiraz Minwalla +5 more
TL;DR: In this paper, the authors show that the spectrum of 1 BPS states in N = 4 Yang-Mills theory, which is known to jump discontinuously from zero to infinitesimal coupling, receives no further renormalization at finite values of the 't Hooft coupling.
Journal ArticleDOI
Accurate Estimation of Neural Population Dynamics without Spike Sorting.
Eric M. Trautmann,Eric M. Trautmann,Sergey D. Stavisky,Subhaneil Lahiri,Katherine Cora Ames,Katherine Cora Ames,Matthew T. Kaufman,Matthew T. Kaufman,Daniel J. O’Shea,Saurabh Vyas,Xulu Sun,Stephen I. Ryu,Stephen I. Ryu,Surya Ganguli,Krishna V. Shenoy +14 more
TL;DR: This work recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons.