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Compressing deep neural networks using a rank-constrained topology.

P. Nakkiran, R. Alvarez, R. Prabhavalkar, and C. Parada. INTERSPEECH, page 1473-1477. ISCA, (2015)

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Optimal systematic distributed storage codes with fast encoding.P. Nakkiran, K. Rashmi, and K. Ramchandran. ISIT, page 430-434. IEEE, (2016)Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting.N. Mallinar, J. Simon, A. Abedsoltan, P. Pandit, M. Belkin, and P. Nakkiran. NeurIPS, (2022)Deconstructing Distributions: A Pointwise Framework of Learning.G. Kaplun, N. Ghosh, S. Garg, B. Barak, and P. Nakkiran. ICLR, OpenReview.net, (2023)How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks.E. Littwin, O. Saremi, M. Advani, V. Thilak, P. Nakkiran, C. Huang, and J. Susskind. CoRR, (2024)Deep Double Descent: Where Bigger Models and More Data HurtP. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever. (2019)cite arxiv:1912.02292Comment: G.K. and Y.B. contributed equally.Algorithmic Polarization for Hidden Markov Models.V. Guruswami, P. Nakkiran, and M. Sudan. ITCS, volume 124 of LIPIcs, page 39:1-39:19. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2019)Knowledge Distillation: Bad Models Can Be Good Role Models.G. Kaplun, E. Malach, P. Nakkiran, and S. Shalev-Shwartz. NeurIPS, (2022)When is Multicalibration Post-Processing Necessary?D. Hansen, S. Devic, P. Nakkiran, and V. Sharan. CoRR, (2024)Loss Minimization Yields Multicalibration for Large Neural Networks.J. Blasiok, P. Gopalan, L. Hu, A. Kalai, and P. Nakkiran. ITCS, volume 287 of LIPIcs, page 17:1-17:21. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2024)Fundamental limits on communication for oblivious updates in storage networks.P. Nakkiran, N. Shah, and K. Rashmi. GLOBECOM, page 2363-2368. IEEE, (2014)

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Compressing deep neural networks using a rank-constrained topology.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Compressing deep neural networks using a rank-constrained topology.

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Compressing deep neural networks using a rank-constrained topology.