Anirvan Sengupta

academic, New High Energy Theory Center

Emailanirvans@rutgers.edu

Source: Scopus
Calculated based on no. of publications stored in Pure and citations from Scopus

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1 Finished

Locating Regulatory Elements in Genomes
Sengupta, A. & SENGUPTA, A. M.
National Human Genome Research Institute
4/13/06 → 3/31/10
Project: Research project

2761 Citations
26 h-Index
55 Article
3 Conference contribution
3 Conference article
2 Chapter
More
- 1 Paper
- 1 Comment/debate

Neurons as Canonical Correlation Analyzers
Pehlevan, C., Zhao, X., Sengupta, A. M. & Chklovskii, D., Jun 30 2020, In: Frontiers in Computational Neuroscience. 14, 55.
Research output: Contribution to journal › Article › peer-review

Open Access
A Neural Network for Semi-supervised Learning on Manifolds
Genkin, A., Sengupta, A. M. & Chklovskii, D., 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 proceeding › Conference contribution
A similarity-preserving neural network trained on transformed images recapitulates salient features of the fly motion detection circuit
Bahroun, Y., Sengupta, A. M. & Chklovskii, D. B., 2019, In: Advances in Neural Information Processing Systems. 32
Research output: Contribution to journal › Conference article › peer-review
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 journal › Article › peer-review
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling
Tepper, M., Sengupta, A. M. & Chklovskii, D., Nov 1 2018, In: Journal of Machine Learning Research. 19
Research output: Contribution to journal › Article › peer-review
3 Scopus citations

Locating Regulatory Elements in Genomes

Neurons as Canonical Correlation Analyzers

A Neural Network for Semi-supervised Learning on Manifolds

A similarity-preserving neural network trained on transformed images recapitulates salient features of the fly motion detection circuit

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

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