5 citations · 15 across the 15 of their papers we have counts for
15 papers
Flexible Online Representation Learning Based on Similarity Matching
Shagesh Sridharan, Yanis Bahroun, Anirvan M. Sengupta
Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data. Such a representation can deal with the dense connect…
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…
Spatial Transformer K-Means
Romain Cosentino, Randall Balestriero, Yanis Bahroun +3
K-means defines one of the most employed centroid-based clustering algorithms with performances tied to the data's embedding. Intricate data embeddings have been designed to push $…
A Normative and Biologically Plausible Algorithm for Independent Component Analysis
Yanis Bahroun, Dmitri B Chklovskii, Anirvan M Sengupta
The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive. In signal processing, linear BSS problems are often solved by Indep…
A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervise…
A Neural Network with Local Learning Rules for Minor Subspace Analysis
Yanis Bahroun, Dmitri B. Chklovskii
The development of neuromorphic hardware and modeling of biological neural networks requires algorithms with local learning rules. Artificial neural networks using local learning r…
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
Romain Cosentino, Randall Balestriero, Yanis Bahroun +3
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of -means applied in the image s…
A biologically plausible neural network for local supervision in cortical microcircuits
Siavash Golkar, David Lipshutz, Yanis Bahroun +2
The backpropagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement, it does not provide a plausible model…
A simple normative network approximates local non-Hebbian learning in the cortex
Siavash Golkar, David Lipshutz, Yanis Bahroun +2
To guide behavior, the brain extracts relevant features from high-dimensional data streamed by sensory organs. Neuroscience experiments demonstrate that the processing of sensory i…
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
David Lipshutz, Yanis Bahroun, Siavash Golkar +2
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that c…
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification
Yanis Bahroun, Andrea Soltoggio
Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous disc…