Projects per year
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 2011
- 1 Finished
Locating Regulatory Elements In Genomes
National Institutes of Health (NIH)
4/13/06 → 3/31/11
Project: Research project
Genome
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 proceeding › Conference 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 journal › Article
Critical Behavior
Mean Squared Error
Universality
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. 19Research output: Contribution to journal › Article
Semidefinite Program
Manifold Learning
Non-negative
Clustering
Gradient methods
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Sengupta, A., 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 journal › Conference article
Neurons
Brain
Neural networks
Computer simulation
Rodentia
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 journal › Article
Learning
Intuition
Neurosciences
Synapses
History