Javier Sagastuy-Breña

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All published works

Action	Title	Year	Authors
+ PDF Chat	The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion	2023	Daniel Kunin Javier Sagastuy-Breña Lauren Gillespie Eshed Margalit Hidenori Tanaka Surya Ganguli Daniel Yamins
+	Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion	2021	Daniel Kunin Javier Sagastuy-Breña Lauren Gillespie Eshed Margalit Hidenori Tanaka Surya Ganguli Daniel Yamins
+	The Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations, and Anomalous Diffusion	2021	Daniel Kunin Javier Sagastuy-Breña Lauren Gillespie Eshed Margalit Hidenori Tanaka Surya Ganguli Daniel Yamins
+	Two Routes to Scalable Credit Assignment without Weight Symmetry	2020	Daniel Kunin Aran Nayebi Javier Sagastuy-Breña Surya Ganguli Jonathan M. Bloom Daniel Yamins
+	Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics	2020	Daniel Kunin Javier Sagastuy-Breña Surya Ganguli Daniel Yamins Hidenori Tanaka

Common Coauthors

Coauthor	Papers Together
Daniel Yamins	5
Daniel Kunin	5
Surya Ganguli	5
Eshed Margalit	3
Hidenori Tanaka	3
Lauren Gillespie	2
Hidenori Tanaka	1
Aran Nayebi	1
Lauren Gillespie	1
Jonathan M. Bloom	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+	Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate	2020	Zhiyuan Li Kaifeng Lyu Sanjeev Arora	3
+	Neural Tangent Kernel: Convergence and Generalization in Neural Networks	2018	Arthur Paul Jacot Franck Gabriel Clément Hongler	3
+	Three Factors Influencing Minima in SGD	2017	Stanisław Jastrzȩbski Zachary Kenton Devansh Arpit Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey	3
+ PDF Chat	Wide neural networks of any depth evolve as linear models under gradient descent <sup>*</sup>	2020	Jaehoon Lee Lechao Xiao Samuel S. Schoenholz Yasaman Bahri Roman Novak Jascha Sohl‐Dickstein Jeffrey Pennington	3
+	Stochastic modified equations and adaptive stochastic gradient algorithms	2015	Qianxiao Li Cheng Tai E Weinan	2
+	On the Origin of Implicit Regularization in Stochastic Gradient Descent	2021	Samuel Smith Benoît Dherin David G. T. Barrett Soham De	2
+ PDF Chat	A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks	2019	Umut Şimşekli Levent Sagun Mert Gürbüzbalaban	2
+	On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)	2021	Zhiyuan Li Sadhika Malladi Sanjeev Arora	2
+	Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour	2017	Priya Goyal Piotr Dollár Ross Girshick Pieter Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He	2
+	Exact solutions to the nonlinear dynamics of learning in deep linear neural networks	2013	Andrew Saxe James L. McClelland Surya Ganguli	2
+	Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond	2016	Levent Sagun Léon Bottou Yann LeCun	2
+	The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size.	2018	Vardan Papyan	2
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	2
+	Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay.	2020	Ruosi Wan Zhanxing Zhu Xiangyu Zhang Jian Sun	2
+	Analysis Of Momentum Methods	2019	Nikola B. Kovachki Andrew M. Stuart	2
+	Fluctuation-dissipation relations for stochastic gradient descent	2018	Sho Yaida	2
+	Don't Decay the Learning Rate, Increase the Batch Size	2017	Samuel Smith Pieter-Jan Kindermans Chris Ying Quoc V. Le	2
+	Deep Neural Networks as Gaussian Processes	2017	Jaehoon Lee Yasaman Bahri Roman Novak Samuel S. Schoenholz Jeffrey Pennington Jascha Sohl‐Dickstein	2
+	One weird trick for parallelizing convolutional neural networks	2014	Alex Krizhevsky	2
+ PDF Chat	Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks	2018	Pratik Chaudhari Stefano Soatto	2
+	The Variational Formulation of the Fokker--Planck Equation	1998	Richard W. Jordan David Kinderlehrer Félix Otto	2
+	Gradient Descent Happens in a Tiny Subspace	2018	Guy Gur-Ari Daniel A. Roberts Ethan Dyer	2
+	On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length	2018	Stanisław Jastrzȩbski Zachary Kenton Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey	2
+	TF-Replicator: Distributed Machine Learning for Researchers	2019	Peter Buchlovsky David Budden Dominik Grewe Chris S. Jones John Aslanides Frederic Besse Andy Brock Aidan Clark Sergio Gómez Colmenarejo Aedan Pope	1
+	Loss Landscapes of Regularized Linear Autoencoders	2019	Daniel Kunin Jonathan M. Bloom Aleksandrina Goeva Cotton Seed	1
+	Width Provably Matters in Optimization for Deep Linear Neural Networks	2019	Simon S. Du Wei Hu	1
+	Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation	2019	Yuanyuan Feng Tingran Gao Lei Li Jian‐Guo Liu Yulong Lu	1
+	Deep Learning without Weight Transport	2019	Mohamed Akrout Collin Wilson Peter C. Humphreys Timothy Lillicrap Douglas Tweed	1
+	Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures	2018	Sergey Bartunov Adam Santoro Blake A. Richards Luke Marris Geoffrey E. Hinton Timothy Lillicrap	1
+	Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model	2019	Guodong Zhang Lala Li Zachary Nado James Martens Sushant Sachdeva George E. Dahl Christopher J. Shallue Roger Grosse	1
+	Stochastic Gradient Descent as Approximate Bayesian Inference	2017	Stephan Mandt Matthew D. Hoffman David M. Blei	1
+ PDF Chat	A mean field view of the landscape of two-layer neural networks	2018	Mei Song Andrea Montanari Phan-Minh Nguyen	1
+	Learning in the machine: Random backpropagation and the deep learning channel	2018	Pierre Baldi Peter Sadowski Zhiqin Lu	1
+	Deep Learning without Poor Local Minima	2016	Kenji Kawaguchi	1
+	Deep Neural Networks as Gaussian Processes	2018	Jaehoon Lee Yasaman Bahri Roman Novak Samuel S. Schoenholz Jeffrey Pennington Jascha Sohl‐Dickstein	1
+	Direct Feedback Alignment Provides Learning in Deep Neural Networks	2016	Arild Nøkland	1
+	Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced	2018	Simon S. Du Wei Hu Jason D. Lee	1
+	A variational analysis of stochastic gradient algorithms	2016	Stephan Mandt Matthew D. Hoffman David M. Blei	1
+	Online Normalization for Training Neural Networks	2019	Vitaliy Chiley Ilya Sharapov Atli Kosson Urs Köster Ryan Reece Sofia Samaniego de la Fuente Vishal Subbiah Michael James	1
+	On Exact Computation with an Infinitely Wide Neural Net	2019	Sanjeev Arora Simon S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang	1
+	A Simple Baseline for Bayesian Uncertainty in Deep Learning	2019	Wesley J. Maddox Pavel Izmailov Timur Garipov Dmitry Vetrov Andrew Gordon Wilson	1
+	An Exponential Learning Rate Schedule for Deep Learning	2019	Zhiyuan Li Sanjeev Arora	1
+	The Implicit Regularization of Stochastic Gradient Flow for Least Squares	2020	Alnur Ali Edgar Dobriban Ryan J. Tibshirani	1
+	Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions	2020	Stefano Sarao Mannelli Eric Vanden‐Eijnden Lenka Zdeborová	1
+ PDF Chat	Anomalous diffusion dynamics of learning in deep neural networks	2022	Guozhang Chen Cheng Qu Pulin Gong	1
+	Implicit Gradient Regularization	2020	David G. T. Barrett Benoît Dherin	1
+	Spherical Motion Dynamics: Learning Dynamics of Neural Network with Normalization, Weight Decay, and SGD	2020	Ruosi Wan Zhanxing Zhu Xiangyu Zhang Jian Sun	1
+ PDF Chat	Stochastic modified equations for the asynchronous stochastic gradient descent	2019	Jing An Jianfeng Lu Lexing Ying	1
+ PDF Chat	Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification*	2021	Francesca Mignacco Florent Krząkała Pierfrancesco Urbani Lenka Zdeborová	1