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1802.06384
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Spurious Valleys in Two-layer Neural Network Optimization Landscapes
18 February 2018
Luca Venturi
Afonso S. Bandeira
Joan Bruna
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Papers citing
"Spurious Valleys in Two-layer Neural Network Optimization Landscapes"
50 / 51 papers shown
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When Expressivity Meets Trainability: Fewer than
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Deep learning, stochastic gradient descent and diffusion maps
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Exponentially Many Local Minima in Quantum Neural Networks
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Xiaodi Wu
366
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Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
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Roger C. Grosse
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22 Apr 2021
Spurious Local Minima Are Common for Deep Neural Networks with Piecewise Linear Activations
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Bo Liu
166
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25 Feb 2021
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
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Yan Li
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238
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24 Feb 2021
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319
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A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks
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Yi Tian Xu
Y. Aflalo
Lihi Zelnik-Manor
Rong Jin
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Towards a Better Global Loss Landscape of GANs
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Tiantian Fang
Alex Schwing
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292
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228
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PDE constraints on smooth hierarchical functions computed by neural networks
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156
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Mengya Shen
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280
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∑
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Xingtai Lv
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129
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431
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Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
International Conference on Machine Learning (ICML), 2019
Aleksandr Shevchenko
Marco Mondelli
487
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20 Dec 2019
Optimization for deep learning: theory and algorithms
Tian Ding
ODL
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19 Dec 2019
Stationary Points of Shallow Neural Networks with Quadratic Activation Function
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Eren C. Kizildag
Ilias Zadik
256
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03 Dec 2019
Sub-Optimal Local Minima Exist for Neural Networks with Almost All Non-Linear Activations
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Dawei Li
Tian Ding
404
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04 Nov 2019
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
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Reinhard Heckel
Mahdi Soltanolkotabi
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301
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31 Oct 2019
Nearly Minimal Over-Parametrization of Shallow Neural Networks
Armin Eftekhari
Chaehwan Song
Volkan Cevher
226
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09 Oct 2019
Pure and Spurious Critical Points: a Geometric Study of Linear Networks
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Matthew Trager
Kathlén Kohn
Joan Bruna
266
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Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations
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Vishnu Suresh Lokhande
Songwong Tasneeyapant
Abhay Venkatesh
Sathya Ravi
Vikas Singh
207
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12 Sep 2019
Additive function approximation in the brain
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262
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Gradient Dynamics of Shallow Univariate ReLU Networks
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Francis Williams
Matthew Trager
Claudio Silva
Daniele Panozzo
Denis Zorin
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204
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18 Jun 2019
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
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Rohith Kuditipudi
Xiang Wang
Holden Lee
Yi Zhang
Zhiyuan Li
Wei Hu
Sanjeev Arora
Rong Ge
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A mean-field limit for certain deep neural networks
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R. Oliveira
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On the Expressive Power of Deep Polynomial Neural Networks
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Joe Kileel
Matthew Trager
Joan Bruna
250
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29 May 2019
Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Yanwei Fu
Chen Liu
Donghao Li
Zuyuan Zhong
Xinwei Sun
Jinshan Zeng
Xingtai Lv
361
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The Landscape of the Planted Clique Problem: Dense subgraphs and the Overlap Gap Property
D. Gamarnik
Ilias Zadik
217
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15 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Mingchen Li
Mahdi Soltanolkotabi
Samet Oymak
NoLa
589
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27 Mar 2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
IEEE Journal on Selected Areas in Information Theory (JSAIT), 2019
Samet Oymak
Mahdi Soltanolkotabi
344
341
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12 Feb 2019
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks
Phan-Minh Nguyen
AI4CE
263
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07 Feb 2019
Depth creates no more spurious local minima
Li Zhang
281
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28 Jan 2019
On Connected Sublevel Sets in Deep Learning
Quynh N. Nguyen
429
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22 Jan 2019
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins
Dawei Li
Tian Ding
Tian Ding
706
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28 Dec 2018
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
Samet Oymak
Mahdi Soltanolkotabi
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360
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A jamming transition from under- to over-parametrization affects loss landscape and generalization
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Mario Geiger
Stéphane dÁscoli
Levent Sagun
Giulio Biroli
Matthieu Wyart
465
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22 Oct 2018
On the loss landscape of a class of deep neural networks with no bad local valleys
International Conference on Learning Representations (ICLR), 2018
Quynh N. Nguyen
Mahesh Chandra Mukkamala
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On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
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