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Fingerprint Dive into the research topics where Anirvan Sengupta is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Chromatin Medicine & Life Sciences
Epigenomics Medicine & Life Sciences
Transcription Factors Medicine & Life Sciences
Binding Sites Medicine & Life Sciences
Yeasts Medicine & Life Sciences
DNA Medicine & Life Sciences
Gene Regulatory Networks Medicine & Life Sciences
SELEX Aptamer Technique Medicine & Life Sciences

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2006 2010

Transcription Factors
Escherichia coli
Sigma Factor
Gene Expression

Research Output 1994 2019

A Neural Network for Semi-supervised Learning on Manifolds

Genkin, A., Sengupta, A. M. & Chklovskii, D., Jan 1 2019, Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Tetko, I. V., Karpov, P., Theis, F. & Kurková, V. (eds.). Springer Verlag, p. 375-386 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11727 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Semi-supervised Learning
Supervised learning
Neural Networks
Neural networks
Feedforward neural networks

Critical Behavior and Universality Classes for an Algorithmic Phase Transition in Sparse Reconstruction

Ramezanali, M., Mitra, P. P. & Sengupta, A. M., May 15 2019, In : Journal of Statistical Physics. 175, 3-4, p. 764-788 25 p.

Research output: Contribution to journalArticle

Critical Behavior
Mean Squared Error
Critical Curves
Phase Transition

Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling

Tepper, M., Sengupta, A. & Chklovskii, D., Nov 1 2018, In : Journal of Machine Learning Research. 19

Research output: Contribution to journalArticle

Semidefinite Program
Manifold Learning
Gradient methods

Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks

Sengupta, A. M., Tepper, M., Pehlevan, C., Genkin, A. & Chklovskii, D. B., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 7080-7090 11 p.

Research output: Contribution to journalConference article

Neural networks
Computer simulation
9 Citations (Scopus)

Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?

Pehlevan, C., Sengupta, A. & Chklovskii, D. B., Jan 1 2018, In : Neural Computation. 30, 1, p. 84-124 41 p.

Research output: Contribution to journalArticle