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Anirvan M. Sengupta
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2020 – today
- 2023
- [i19]David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity. CoRR abs/2302.10051 (2023) - [i18]Yanis Bahroun, Shagesh Sridharan, Atithi Acharya, Dmitri B. Chklovskii, Anirvan M. Sengupta:
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training. CoRR abs/2308.02427 (2023) - [i17]Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta:
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning. CoRR abs/2309.16687 (2023) - 2022
- [j14]Tiberiu Tesileanu, Siavash Golkar, Samaneh Nasiri, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neural Circuits for Dynamics-Based Segmentation of Time Series. Neural Comput. 34(4): 891-938 (2022) - [c12]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Spatial Transformer K-Means. IEEECONF 2022: 1444-1448 - [c11]Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy. NeurIPS 2022 - [i16]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Spatial Transformer K-Means. CoRR abs/2202.07829 (2022) - [i15]Romain Cosentino, Anirvan M. Sengupta, Salman Avestimehr, Mahdi Soltanolkotabi
, Antonio Ortega, Theodore L. Willke, Mariano Tepper:
Toward a Geometrical Understanding of Self-supervised Contrastive Learning. CoRR abs/2205.06926 (2022) - 2021
- [j13]David Lipshutz
A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis. Neural Comput. 33(9): 2309-2352 (2021) - [c10]Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta:
A Normative and Biologically Plausible Algorithm for Independent Component Analysis. NeurIPS 2021: 7368-7384 - [c9]Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neural optimal feedback control with local learning rules. NeurIPS 2021: 16358-16370 - [i14]Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit. CoRR abs/2102.05503 (2021) - [i13]Atithi Acharya, Siddhartha Saha, Anirvan M. Sengupta:
Informationally complete POVM-based shadow tomography. CoRR abs/2105.05992 (2021) - [i12]Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neural optimal feedback control with local learning rules. CoRR abs/2111.06920 (2021) - [i11]Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta:
A Normative and Biologically Plausible Algorithm for Independent Component Analysis. CoRR abs/2111.08858 (2021) - 2020
- [j12]Cengiz Pehlevan, Xinyuan Zhao
Neurons as Canonical Correlation Analyzers. Frontiers Comput. Neurosci. 14: 55 (2020) - [c8]Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A simple normative network approximates local non-Hebbian learning in the cortex. NeurIPS 2020 - [i10]David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A biologically plausible neural network for multi-channel Canonical Correlation Analysis. CoRR abs/2010.00525 (2020) - [i9]Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A simple normative network approximates local non-Hebbian learning in the cortex. CoRR abs/2010.12660 (2020) - [i8]Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A biologically plausible neural network for local supervision in cortical microcircuits. CoRR abs/2011.15031 (2020) - [i7]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Interpretable Image Clustering via Diffeomorphism-Aware K-Means. CoRR abs/2012.09743 (2020)
2010 – 2019
- 2019
- [c7]Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A Neural Network for Semi-supervised Learning on Manifolds. ICANN (1) 2019: 375-386 - [c6]Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta:
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit. NeurIPS 2019: 14178-14189 - [i6]Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A Neural Network for Semi-Supervised Learning on Manifolds. CoRR abs/1908.08145 (2019) - 2018
- [j11]Mariano Tepper, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling. J. Mach. Learn. Res. 19: 82:1-82:30 (2018) - [j10]Cengiz Pehlevan
Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks? Neural Comput. 30(1) (2018) - [c5]Anirvan M. Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri B. Chklovskii:
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks. NeurIPS 2018: 7080-7090 - 2017
- [c4]Cengiz Pehlevan
Resource-efficient perceptron has sparse synaptic weight distribution. SIU 2017: 1-4 - [i5]Cengiz Pehlevan, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Adversarial synapses: Hebbian/anti-Hebbian learning optimizes min-max objectives. CoRR abs/1703.07914 (2017) - [i4]Mariano Tepper, Anirvan M. Sengupta, Dmitri B. Chklovskii:
The surprising secret identity of the semidefinite relaxation of K-means: manifold learning. CoRR abs/1706.06028 (2017) - 2016
- [c3]Mohammad Ramezanali, Partha P. Mitra
Mean field analysis of sparse reconstruction with correlated variables. EUSIPCO 2016: 1267-1271 - 2015
- [i3]Mohammad Ramezanali, Partha P. Mitra, Anirvan M. Sengupta:
The cavity method for phase transitions in sparse reconstruction algorithms. CoRR abs/1501.03194 (2015) - [i2]Mohammad Ramezanali, Partha P. Mitra, Anirvan M. Sengupta:
Critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction. CoRR abs/1509.08995 (2015) - 2013
- [j9]Swagatam Mukhopadhyay, Anirvan M. Sengupta:
The Role of Multiple Marks in Epigenetic Silencing and the Emergence of a Stable Bivalent Chromatin State. PLoS Comput. Biol. 9(7) (2013) - 2012
- [j8]Rajat Shuvro Roy, Kevin C. Chen, Anirvan M. Sengupta, Alexander Schliep
SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. J. Comput. Biol. 19(10): 1162-1175 (2012) - 2011
- [j7]R. Scott McIsaac, Kerwyn Casey Huang, Anirvan M. Sengupta, Ned S. Wingreen:
Does the Potential for Chaos Constrain the Embryonic Cell-Cycle Oscillator? PLoS Comput. Biol. 7(7) (2011) - [i1]Rajat Shuvro Roy, Kevin C. Chen, Anirvan M. Sengupta, Alexander Schliep:
SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. CoRR abs/1111.1426 (2011) - 2010
- [j6]Swagatam Mukhopadhyay, Vijayalakshmi H. Nagaraj, Anirvan M. Sengupta:
Locus dependence in epigenetic chromatin silencing. Biosyst. 102(1): 49-54 (2010) - [j5]Adel Dayarian, Todd P. Michael
SOPRA: Scaffolding algorithm for paired reads via statistical optimization. BMC Bioinform. 11: 345 (2010)
2000 – 2009
- 2009
- [j4]Amar Drawid, Nupur Gupta, Vijayalakshmi H. Nagaraj, Céline Gélinas, Anirvan M. Sengupta:
OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif. BMC Bioinform. 10 (2009) - [j3]Adel Dayarian, Madalena Chaves
Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks. PLoS Comput. Biol. 5(1) (2009) - 2005
- [j2]Ruadhan A. O'Flanagan, Guillaume Paillard, Richard Lavery
Non-additivity in protein-DNA binding. Bioinform. 21(10): 2254-2263 (2005) - 2003
- [j1]Aris L. Moustakas, Steven H. Simon
MIMO capacity through correlated channels in the presence of correlated interferers and noise: a (not so) large N analysis. IEEE Trans. Inf. Theory 49(10): 2545-2561 (2003) - [c2]Aris L. Moustakas, Steven H. Simon, Anirvan M. Sengupta:
Distribution of MIMO capacity in the presence of correlated signals and interferers: A (not so) large N analysis. Multiantenna Channels: Capacity, Coding and Signal Processing 2003: 43-55 - [c1]Aris L. Moustakas, Steven H. Simon, Anirvan M. Sengupta:
A model to calculate the capacity distribution of correlated MIMO channels and interferers. GLOBECOM 2003: 1791-1796
Coauthor Index
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