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12![](https://www.pdfsearch.io/img/2cc7baa5cd7f83b2fe810ceba573c3e7.jpg) | Add to Reading ListSource URL: www.work.caltech.eduLanguage: English - Date: 2015-01-01 11:20:46
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13![A Variational Analysis of Stochastic Gradient Algorithms Stephan Mandt Columbia University, Data Science Institute, New York, USA SM 3976@ COLUMBIA . EDU A Variational Analysis of Stochastic Gradient Algorithms Stephan Mandt Columbia University, Data Science Institute, New York, USA SM 3976@ COLUMBIA . EDU](https://www.pdfsearch.io/img/b70dfcc81b043877b19c3b3a25b6548a.jpg) | Add to Reading ListSource URL: jmlr.orgLanguage: English - Date: 2016-07-20 01:41:10
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14![Simple, Efficient, and Neural Algorithms for Sparse Coding Sanjeev Arora∗ Princeton University, Computer Science Department Simple, Efficient, and Neural Algorithms for Sparse Coding Sanjeev Arora∗ Princeton University, Computer Science Department](https://www.pdfsearch.io/img/f929df384a6f22a0c4a496986f0545e7.jpg) | Add to Reading ListSource URL: jmlr.orgLanguage: English - Date: 2015-07-20 20:08:35
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15![Journal of Machine Learning Research2159 Submitted 3/10; Revised 3/11; Published 7/11 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization∗ Journal of Machine Learning Research2159 Submitted 3/10; Revised 3/11; Published 7/11 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization∗](https://www.pdfsearch.io/img/4ece466cb398369dc07cc3468930963b.jpg) | Add to Reading ListSource URL: www.jmlr.orgLanguage: English - Date: 2011-07-05 16:26:18
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16![Revisiting natural gradient for deep networks Yoshua Bengio Universit´e de Montr´eal Montr´eal QC H3C 3J7 Canada Revisiting natural gradient for deep networks Yoshua Bengio Universit´e de Montr´eal Montr´eal QC H3C 3J7 Canada](https://www.pdfsearch.io/img/d836460c8fc5fec8bd099cecd4636c27.jpg) | Add to Reading ListSource URL: arxiv.orgLanguage: English - Date: 2014-02-18 01:51:44
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17![Batch-Incremental vs. Instance-Incremental Learning in Dynamic and Evolving Data Jesse Read1 , Albert Bifet2 , Bernhard Pfahringer2 , Geoff Holmes2 1 Department of Signal Theory and Communications Batch-Incremental vs. Instance-Incremental Learning in Dynamic and Evolving Data Jesse Read1 , Albert Bifet2 , Bernhard Pfahringer2 , Geoff Holmes2 1 Department of Signal Theory and Communications](https://www.pdfsearch.io/img/e80b810bb5dfc531205410cae1bf1256.jpg) | Add to Reading ListSource URL: users.ics.aalto.fiLanguage: English - Date: 2012-10-25 03:54:39
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18![CS168: The Modern Algorithmic Toolbox Lecture #6: Stochastic Gradient Descent and Regularization Tim Roughgarden & Gregory Valiant∗ April 13, 2016 CS168: The Modern Algorithmic Toolbox Lecture #6: Stochastic Gradient Descent and Regularization Tim Roughgarden & Gregory Valiant∗ April 13, 2016](https://www.pdfsearch.io/img/212337f32f0532d80acd180dc892f57d.jpg) | Add to Reading ListSource URL: theory.stanford.eduLanguage: English - Date: 2016-06-04 09:49:44
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19![JMLR: Workshop and Conference Proceedings vol 40:1–46, 2015 Escaping From Saddle Points – Online Stochastic Gradient for Tensor Decomposition Rong Ge JMLR: Workshop and Conference Proceedings vol 40:1–46, 2015 Escaping From Saddle Points – Online Stochastic Gradient for Tensor Decomposition Rong Ge](https://www.pdfsearch.io/img/031567571076e4de133ca3d1374c22d3.jpg) | Add to Reading ListSource URL: jmlr.orgLanguage: English - Date: 2015-07-20 20:08:36
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20![Advances in the Minimization of Finite Sums Mark Schmidt Joint work with Nicolas Le Roux, Francis Bach, Reza Babanezhad and Mohamed Ahmed University of British Columbia
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