91 citations · 566 across the 39 of their papers we have counts for
40 papers
Learning the eigenstructure of quantum dynamics using classical shadows
Atithi Acharya, Siddhartha Saha, Shagesh Sridharan +2
Learning dynamics from repeated observation of the time evolution of an open quantum system, namely, the problem of quantum process tomography is an important task. This task is di…
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning
Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta
A normative approach called Similarity Matching was recently introduced for deriving and understanding the algorithmic basis of neural computation focused on unsupervised problems.…
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training
Yanis Bahroun, Shagesh Sridharan, Atithi Acharya +2
While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result,…
Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity
David Lipshutz, Yanis Bahroun, Siavash Golkar +2
An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate…
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun +2
Predictive coding has emerged as an influential normative model of neural computation, with numerous extensions and applications. As such, much effort has been put into mapping PC…
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Romain Cosentino, Anirvan Sengupta, Salman Avestimehr +4
Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotatio…