Distribution of MIMO capacity in the presence of correlated signals and interferers: A (not so) large N analysis. - Aris L. Moustakas; Steven H. Simon; Anirvan M. Sengupta (Multiantenna Channels: Capacity, Coding and Signal Processing, 2003)

A model to calculate the capacity distribution of correlated MIMO channels and interferers. - Aris L. Moustakas; Steven H. Simon; Anirvan M. Sengupta (GLOBECOM, 2003)

MIMO capacity through correlated channels in the presence of correlated interferers and noise: a (not so) large N analysis. - Aris L. Moustakas; Steven H. Simon; Anirvan M. Sengupta (IEEE Trans. Inf. Theory, 2003)

Non-additivity in protein-DNA binding. - Ruadhan A. O'Flanagan; Guillaume Paillard; Richard Lavery; Anirvan M. Sengupta (Bioinform., 2005)

OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif. - Amar Drawid; Nupur Gupta; Vijayalakshmi H. Nagaraj; Céline Gélinas; Anirvan M. Sengupta (BMC Bioinform., 2009)

Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks. - Adel Dayarian; Madalena Chaves; Eduardo D. Sontag; Anirvan M. Sengupta (PLoS Comput. Biol., 2009)

Locus dependence in epigenetic chromatin silencing. - Swagatam Mukhopadhyay; Vijayalakshmi H. Nagaraj; Anirvan M. Sengupta (Biosyst., 2010)

SOPRA: Scaffolding algorithm for paired reads via statistical optimization. - Adel Dayarian; Todd P. Michael; Anirvan M. Sengupta (BMC Bioinform., 2010)

Does the Potential for Chaos Constrain the Embryonic Cell-Cycle Oscillator? - R. Scott McIsaac; Kerwyn Casey Huang; Anirvan M. Sengupta; Ned S. Wingreen (PLoS Comput. Biol., 2011)

SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. - Rajat Shuvro Roy; Kevin C. Chen; Anirvan M. Sengupta; Alexander Schliep (J. Comput. Biol., 2012)

The Role of Multiple Marks in Epigenetic Silencing and the Emergence of a Stable Bivalent Chromatin State. - Swagatam Mukhopadhyay; Anirvan M. Sengupta (PLoS Comput. Biol., 2013)

The cavity method for phase transitions in sparse reconstruction algorithms. - Mohammad Ramezanali; Partha P. Mitra; Anirvan M. Sengupta (CoRR, 2015)

Critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction. - Mohammad Ramezanali; Partha P. Mitra; Anirvan M. Sengupta (CoRR, 2015)

Mean field analysis of sparse reconstruction with correlated variables. - Mohammad Ramezanali; Partha P. Mitra; Anirvan M. Sengupta (EUSIPCO, 2016)

Resource-efficient perceptron has sparse synaptic weight distribution. - Cengiz Pehlevan; Anirvan M. Sengupta (SIU, 2017)

Adversarial synapses: Hebbian/anti-Hebbian learning optimizes min-max objectives. - Cengiz Pehlevan; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2017)

The surprising secret identity of the semidefinite relaxation of K-means: manifold learning. - Mariano Tepper; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2017)

Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks. - Anirvan M. Sengupta; Cengiz Pehlevan; Mariano Tepper; Alexander Genkin; Dmitri B. Chklovskii (NeurIPS, 2018)

Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling. - Mariano Tepper; Anirvan M. Sengupta; Dmitri B. Chklovskii (J. Mach. Learn. Res., 2018)

Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks? - Cengiz Pehlevan; Anirvan M. Sengupta; Dmitri B. Chklovskii (Neural Comput., 2018)

A Neural Network for Semi-supervised Learning on Manifolds. - Alexander Genkin; Anirvan M. Sengupta; Dmitri B. Chklovskii (ICANN (1), 2019)

A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit. - Yanis Bahroun; Dmitri B. Chklovskii; Anirvan M. Sengupta (NeurIPS, 2019)

A simple normative network approximates local non-Hebbian learning in the cortex. - Siavash Golkar; David Lipshutz; Yanis Bahroun; Anirvan M. Sengupta; Dmitri B. Chklovskii (NeurIPS, 2020)

A biologically plausible neural network for multi-channel Canonical Correlation Analysis. - David Lipshutz; Yanis Bahroun; Siavash Golkar; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2020)

A biologically plausible neural network for local supervision in cortical microcircuits. - Siavash Golkar; David Lipshutz; Yanis Bahroun; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2020)

Interpretable Image Clustering via Diffeomorphism-Aware K-Means. - Romain Cosentino; Randall Balestriero; Yanis Bahroun; Anirvan M. Sengupta; Richard G. Baraniuk; Behnaam Aazhang (CoRR, 2020)

Neurons as Canonical Correlation Analyzers. - Cengiz Pehlevan; Xinyuan Zhao; Anirvan M. Sengupta; Dmitri B. Chklovskii (Frontiers Comput. Neurosci., 2020)

A Normative and Biologically Plausible Algorithm for Independent Component Analysis. - Yanis Bahroun; Dmitri B. Chklovskii; Anirvan M. Sengupta (NeurIPS, 2021)

Neural optimal feedback control with local learning rules. - Johannes Friedrich; Siavash Golkar; Shiva Farashahi; Alexander Genkin; Anirvan M. Sengupta; Dmitri B. Chklovskii (NeurIPS, 2021)

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 (CoRR, 2021)

Informationally complete POVM-based shadow tomography. - Atithi Acharya; Siddhartha Saha; Anirvan M. Sengupta (CoRR, 2021)

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis. - David Lipshutz; Yanis Bahroun; Siavash Golkar; Anirvan M. Sengupta; Dmitri B. Chklovskii (Neural Comput., 2021)

Spatial Transformer K-Means. - Romain Cosentino; Randall Balestriero; Yanis Bahroun; Anirvan M. Sengupta; Richard G. Baraniuk; Behnaam Aazhang (IEEECONF, 2022)

Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy. - Siavash Golkar; Tiberiu Tesileanu; Yanis Bahroun; Anirvan M. Sengupta; Dmitri B. Chklovskii (NeurIPS, 2022)

Toward a Geometrical Understanding of Self-supervised Contrastive Learning. - Romain Cosentino; Anirvan M. Sengupta; Salman Avestimehr; Mahdi Soltanolkotabi; Antonio Ortega; Theodore L. Willke; Mariano Tepper (CoRR, 2022)

Neural Circuits for Dynamics-Based Segmentation of Time Series. - Tiberiu Tesileanu; Siavash Golkar; Samaneh Nasiri; Anirvan M. Sengupta; Dmitri B. Chklovskii (Neural Comput., 2022)

A normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity. - David Lipshutz; Yanis Bahroun; Siavash Golkar; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2023)

Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training. - Yanis Bahroun; Shagesh Sridharan; Atithi Acharya; Dmitri B. Chklovskii; Anirvan M. Sengupta (CoRR, 2023)

Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning. - Yanis Bahroun; Dmitri B. Chklovskii; Anirvan M. Sengupta (CoRR, 2023)

Deep Learning Based Superconductivity: Prediction and Experimental Tests. - Daniel Kaplan; Adam Zhang; Joanna Blawat; Rongying Jin; Robert J. Cava; Viktor Oudovenko; Gabriel Kotliar; Anirvan M. Sengupta; Weiwei Xie (CoRR, 2024)

Numerical results for "Compressing the two-particle Green's function using wavelets: Theory and application to the Hubbard atom". - Emin Moghadas; Nikolaus Dräger; Alessandro Toschi; Jiawei Zang; Matija Medvidovic; Dominik Kiese; Andrew J. Millis; Anirvan M. Sengupta; Sabine Andergassen; Domenico Di Sante (TU Wien Research Data, 2024)

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval. - Matthew Smart; Alberto Bietti; Anirvan M. Sengupta (CoRR, 2025)