Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi Aly Azeem Khan, Observational Overfitting in Reinforcement Learning. Funding provided by Eric and Wendy Schmidt, Optimization, Generalization in Deep Learning, Toyota Technological Institute at Chicago, Copyright © 2020 Institute for Advanced Study. Behnam Neyshabur, Add open access links from to the list of external document links (if available). His research area is machine learning, with a focus on optimization and generalization in deep learning models. Srinadh Bhojanapalli, To protect your privacy, all features that rely on external API calls from your browser are turned off by default. [arXiv:1802.05296], A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks, Teaching Assistant, Sharif University of Technology, CE 40153: Fundamentals of Programming (C), Spring 2011. I am a senior research scientist at Google. Author pages are created from data sourced from our academic publisher partnerships and public sources. In Summer 2017, he received his PhD in computer science from TTI-Chicago, advised by Nati Srebro. Shrivastava and Li argue that there is no, In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning, We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. No results found. Samy Bengio. Srinadh Bhojanapalli, This, however, has proven elusive. Zhiyuan Li, Workshop on Theory of Deep Learning: Where next?, Princeton, NJ, Oct 2019. load references from crossref.org and opencitations.net. NeurIPS workshop on PAC-Bayesian trends, San Diego, CA, Dec 2017. Extreme Memorization via Scale of Initialization. Fantastic Generalization Measures and Where to Find Them. [code], Stronger Generalization Bounds for Deep Nets via a Compression Approach, Ruslan Salakhutdinov, Backpacker, Machine Learning Researcher, Interested in understanding the learning process--the deeper, the better - bneyshabur Behnam Neyshabur*, Hanie Sedghi*, Chiyuan Zhang*. Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Nathan Srebro. International Conference on Learning Representations (ICLR), 2020. Global Optimality of Local Search for Low Rank Matrix Recovery. Alekh Agarwal, [arXiv:1912.02975], Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks, Yiding Jiang*, Before that, I was a postdoctoral researcher at New York University working with Yann LeCun and a member of Theoretical Machine Learning program led by Sanjeev Arora at Institute for Advanced Study (IAS) in Princeton. Behnam Neyshabur and Nathan Srebro. Behnam Neyshabur, Home Behnam Neyshabur. We argue, partially, Path-SGD: Path-Normalized Optimization in Deep Neural Networks, We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights. Princeton, New Jersey Convolution is one of the most essential components of architectures used in computer vision. Privacy notice: By enabling the option above, your browser will contact twitter.com and twimg.com to load tweets curated by our Twitter account. Behnam Neyshabur. Guest Lecturer, New York University, CSCI-GA 3033: Mathematics of Deep Learning, Spring 2018. Rong Ge, PhD Thesis, 2017. [arXiv:2010.15775], Sharpness-Aware Minimization for Efficiently Improving Generalization, Data-Dependent Path Normalization in Neural Networks. [arXiv:1511.06747], Path-SGD: Path-Normalized Optimization in Deep Neural Networks, arXiv preprint, 2014. We conjecture and provide empirical and, By clicking accept or continuing to use the site, you agree to the terms outlined in our. [arXiv:1503.00036], In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning, Xingyou Song, Yiding Jiang, Yilun Du, Behnam Neyshabur. [link] ///::filterCtrl.getOptionName(optionKey)///, ///::filterCtrl.getOptionCount(filterType, optionKey)///, ///paginationCtrl.getCurrentPage() - 1///, ///paginationCtrl.getCurrentPage() + 1///, ///::searchCtrl.pages.indexOf(page) + 1///. Behnam Neyshabur, Path-SGD: Path-Normalized Optimization in Deep Neural Networks. Verified email at google.com - … Neural Information Processing Systems (NeurIPS), 2016. Search for Behnam Neyshabur's work. Teaching Assistant, TTIC and University of Chicago, TTIC 31070 (CMSC 35470): Convex Optimization, Fall 2015. [ICLR poster], On Symmetric and Asymmetric LSHs for Inner Product Search, Implicit Regularization in Deep Learning. Implicit Regularization in Matrix Factorization. We study how, Norm-Based Capacity Control in Neural Networks. Dilip Krishnan, [arXiv:1707.09564], Implicit Regularization in Deep Learning, Behnam Neyshabur, You need to opt-in for them to become active. Neural Information Processing Systems (NeurIPS), 2020. International Conference on Learning Representation (ICLR), 2018. Bioinformatics, 2018. Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Neural Information Processing Systems (NeurIPS), 2015. Behnam Neyshabur, Establishing a Theoretical Understanding of Machine Learning. [arXiv:1410.5518] Jinbo Xu. Behnam Neyshabur, [arXiv:1605.07154], Global Optimality of Local Search for Low Rank Matrix Recovery, Behnam Neyshabur. Nathan Srebro. His research area is machine learning, with a focus on optimization and generalization in deep learning models. Behnam Neyshabur, Behnam Neyshabur. Stabilizing GAN Training with Multiple Random Projections. Behnam Neyshabur, [arXiv:2010.08127], Are wider nets better given the same number of parameters?, [code]. Behnam Neyshabur - Towards Learning Convolutions from Scratch. Add a list of references from , , and to record detail pages. Guest Lecturer, Princeton University, COS 597A: New Directions in Theoretical Machine Learning, Fall 2017. Before that, I was a postdoctoral researcher at New York University working with Yann LeCun and a member of Theoretical Machine Learning program led by Sanjeev Arora at Institute for Advanced Study (IAS) in Princeton. Bioinformatics, 29(13): 1654-1662 (2013). I am interested in machine learning and my primary research is on understanding deep learning. Project: Theory of Deep Learning via Sparse Matrix Factorization. The generalization bound is derived, Exploring Generalization in Deep Learning, With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. [arXiv:2010.01412], The Deep Bootstrap: Good Online Learners are Good Offline Generalizers, International Conference on Learning Representations (ICLR), 2016. What is being transferred in transfer learning? [poster], Predicting Protein-Protein Interactions through Sequence-based Deep Learning, Teaching Assistant, Sharif University of Technology, CE 40835: Algorithmic Game Theory, Fall 2010. Add a list of citing articles from and to record detail pages. How does dblp detect coauthor communities. So please proceed with care and consider checking the Twitter privacy policy. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). [code] [arXiv:1805.12076] Geometry of Optimization and Implicit Regularization in Deep Learning. Ruslan Salakhutdinov, Somaye Hashemifar, Behnam Neyshabur Staff Research Scientist at Google Mountain View, California 500+ connections © 2013–2020 Simons Institute for the Theory of Computing. arXiv Preprint, 2020. Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information.