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1602.07320
Cited By

Stuck in a What? Adventures in Weight Space

Stuck in a What? Adventures in Weight Space

23 February 2016

Zachary Chase Lipton

ArXiv (abs)PDF HTML

Papers citing "Stuck in a What? Adventures in Weight Space"

10 / 10 papers shown

Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature
Learning and Lazy Training

Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training

Leonardo Petrini

211

12

0

30 Dec 2020

Classifying the classifier: dissecting the weight space of neural
networks

Classifying the classifier: dissecting the weight space of neural networksEuropean Conference on Artificial Intelligence (ECAI), 2020

Gabriel Eilertsen

Timo Ropinski

Anders Ynnerman

245

64

0

13 Feb 2020

Luck Matters: Understanding Training Dynamics of Deep ReLU Networks

Luck Matters: Understanding Training Dynamics of Deep ReLU Networks

433

28

0

31 May 2019

Scaling description of generalization with number of parameters in deep
learning

Scaling description of generalization with number of parameters in deep learning

Stéphane dÁscoli

Clément Hongler

453

208

0

06 Jan 2019

A jamming transition from under- to over-parametrization affects loss
landscape and generalization

A jamming transition from under- to over-parametrization affects loss landscape and generalization

Stéphane dÁscoli

468

161

0

22 Oct 2018

The jamming transition as a paradigm to understand the loss landscape of
deep neural networks

The jamming transition as a paradigm to understand the loss landscape of deep neural networksPhysical Review E (PRE), 2018

Stéphane dÁscoli

473

153

0

25 Sep 2018

PCA of high dimensional random walks with comparison to neural network
training

PCA of high dimensional random walks with comparison to neural network training

Jascha Narain Sohl-Dickstein

155

29

0

22 Jun 2018

Comparing Dynamics: Deep Neural Networks versus Glassy Systems

Comparing Dynamics: Deep Neural Networks versus Glassy Systems

Gerard Ben Arous

454

127

0

19 Mar 2018

Visualizing the Loss Landscape of Neural Nets

Visualizing the Loss Landscape of Neural NetsNeural Information Processing Systems (NeurIPS), 2017

Hao Li

Christoph Studer

836

2,247

0

28 Dec 2017

Distributed Bayesian Learning with Stochastic Natural-gradient
Expectation Propagation and the Posterior Server

Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

Leonard Hasenclever

Thibaut Lienart

Sebastian J. Vollmer

Balaji Lakshminarayanan

Charles Blundell

498

73

0

31 Dec 2015

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