• 2678 Citations
  • 26 h-Index
19942019

Research output per year

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Projects

  • Research Output

    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

  • 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

  • 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 journalArticle

  • 2 Scopus citations

    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

  • 5 Scopus citations

    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

  • 13 Scopus citations