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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
v1v2v3v4 (latest)

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Neural Information Processing Systems (NeurIPS), 2018
27 February 2018
T. Garipov
Pavel Izmailov
Dmitrii Podoprikhin
Dmitry Vetrov
A. Wilson
    UQCV
ArXiv (abs)PDFHTML

Papers citing "Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs"

50 / 548 papers shown
Federated Learning for Multi-Center Imaging Diagnostics: A Study in
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Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
Akis Linardos
Kaisar Kushibar
S. Walsh
P. Gkontra
Karim Lekadir
FedML
166
93
0
07 Jul 2021
What can linear interpolation of neural network loss landscapes tell us?
What can linear interpolation of neural network loss landscapes tell us?International Conference on Machine Learning (ICML), 2021
Tiffany J. Vlaar
Jonathan Frankle
MoMe
230
30
0
30 Jun 2021
Deep Ensembling with No Overhead for either Training or Testing: The
  All-Round Blessings of Dynamic Sparsity
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic SparsityInternational Conference on Learning Representations (ICLR), 2021
Shiwei Liu
Tianlong Chen
Zahra Atashgahi
Xiaohan Chen
Ghada Sokar
Elena Mocanu
Mykola Pechenizkiy
Zinan Lin
Decebal Constantin Mocanu
OOD
381
62
0
28 Jun 2021
Improving Uncertainty Calibration of Deep Neural Networks via Truth
  Discovery and Geometric Optimization
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Chunwei Ma
Ziyun Huang
Jiayi Xian
Mingchen Gao
Jinhui Xu
UQCV
181
16
0
25 Jun 2021
Repulsive Deep Ensembles are Bayesian
Repulsive Deep Ensembles are BayesianNeural Information Processing Systems (NeurIPS), 2021
Francesco DÁngelo
Vincent Fortuin
UQCVBDL
437
116
0
22 Jun 2021
Extracting Global Dynamics of Loss Landscape in Deep Learning Models
Extracting Global Dynamics of Loss Landscape in Deep Learning Models
Mohammed Eslami
Hamed Eramian
Marcio Gameiro
W. Kalies
Konstantin Mischaikow
163
2
0
14 Jun 2021
Revisiting Model Stitching to Compare Neural Representations
Revisiting Model Stitching to Compare Neural RepresentationsNeural Information Processing Systems (NeurIPS), 2021
Yamini Bansal
Preetum Nakkiran
Boaz Barak
FedML
327
152
0
14 Jun 2021
Solving hybrid machine learning tasks by traversing weight space
  geodesics
Solving hybrid machine learning tasks by traversing weight space geodesics
G. Raghavan
Matt Thomson
90
0
0
05 Jun 2021
Greedy Bayesian Posterior Approximation with Deep Ensembles
Greedy Bayesian Posterior Approximation with Deep Ensembles
A. Tiulpin
Matthew B. Blaschko
UQCVFedML
224
4
0
29 May 2021
Geometry of the Loss Landscape in Overparameterized Neural Networks:
  Symmetries and Invariances
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and InvariancesInternational Conference on Machine Learning (ICML), 2021
Berfin cSimcsek
François Ged
Arthur Jacot
Francesco Spadaro
Clément Hongler
W. Gerstner
Johanni Brea
AI4CE
290
119
0
25 May 2021
AirNet: Neural Network Transmission over the Air
AirNet: Neural Network Transmission over the AirIEEE Transactions on Wireless Communications (IEEE TWC), 2021
Mikolaj Jankowski
Deniz Gunduz
K. Mikolajczyk
455
1
0
24 May 2021
Advances in Multi-Variate Analysis Methods for New Physics Searches at
  the Large Hadron Collider
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron ColliderReviews in Physics (Rev. Phys.), 2021
A. Stakia
T. Dorigo
G. Banelli
D. Bortoletto
A. Casa
...
G. Strong
C. Tosciri
J. Varela
Pietro Vischia
A. Weiler
145
5
0
16 May 2021
Structured Ensembles: an Approach to Reduce the Memory Footprint of
  Ensemble Methods
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble MethodsNeural Networks (NN), 2021
Jary Pomponi
Simone Scardapane
A. Uncini
UQCV
189
9
0
06 May 2021
What Are Bayesian Neural Network Posteriors Really Like?
What Are Bayesian Neural Network Posteriors Really Like?International Conference on Machine Learning (ICML), 2021
Pavel Izmailov
Sharad Vikram
Matthew D. Hoffman
A. Wilson
UQCVBDL
331
435
0
29 Apr 2021
Policy Manifold Search: Exploring the Manifold Hypothesis for
  Diversity-based Neuroevolution
Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based NeuroevolutionAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2021
Nemanja Rakićević
Antoine Cully
Petar Kormushev
121
37
0
27 Apr 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss
  Landscapes
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
R. Zemel
Roger C. Grosse
MoMe
455
29
0
22 Apr 2021
Multivariate Deep Evidential Regression
Multivariate Deep Evidential Regression
N. Meinert
Alexander Lavin
BDLPEREDLUQCV
374
29
0
13 Apr 2021
MISA: Online Defense of Trojaned Models using Misattributions
MISA: Online Defense of Trojaned Models using MisattributionsAsia-Pacific Computer Systems Architecture Conference (ACSA), 2021
Panagiota Kiourti
Wenchao Li
Anirban Roy
Karan Sikka
Susmit Jha
250
10
0
29 Mar 2021
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep SubnetworksIEEE International Conference on Computer Vision (ICCV), 2021
Alexandre Ramé
Rémy Sun
Matthieu Cord
UQCV
346
63
0
10 Mar 2021
Nondeterminism and Instability in Neural Network Optimization
Nondeterminism and Instability in Neural Network OptimizationInternational Conference on Machine Learning (ICML), 2021
Cecilia Summers
M. Dinneen
181
51
0
08 Mar 2021
Efficient Model Performance Estimation via Feature Histories
Efficient Model Performance Estimation via Feature Histories
Shengcao Cao
Xiaofang Wang
Kris Kitani
181
1
0
07 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness
Fixing Data Augmentation to Improve Adversarial Robustness
Sylvestre-Alvise Rebuffi
Sven Gowal
D. A. Calian
Florian Stimberg
Olivia Wiles
Timothy A. Mann
AAML
278
301
0
02 Mar 2021
Spurious Local Minima Are Common for Deep Neural Networks with Piecewise
  Linear Activations
Spurious Local Minima Are Common for Deep Neural Networks with Piecewise Linear ActivationsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Bo Liu
132
10
0
25 Feb 2021
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Loss Surface Simplexes for Mode Connecting Volumes and Fast EnsemblingInternational Conference on Machine Learning (ICML), 2021
Gregory W. Benton
Wesley J. Maddox
Sanae Lotfi
A. Wilson
UQCV
358
85
0
25 Feb 2021
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix
  Factorization
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix FactorizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Tianyi Liu
Yan Li
S. Wei
Enlu Zhou
T. Zhao
166
18
0
24 Feb 2021
Learning Neural Network Subspaces
Learning Neural Network SubspacesInternational Conference on Machine Learning (ICML), 2021
Mitchell Wortsman
Maxwell Horton
Carlos Guestrin
Ali Farhadi
Mohammad Rastegari
UQCV
335
97
0
20 Feb 2021
When Are Solutions Connected in Deep Networks?
When Are Solutions Connected in Deep Networks?Neural Information Processing Systems (NeurIPS), 2021
Quynh N. Nguyen
Pierre Bréchet
Marco Mondelli
366
10
0
18 Feb 2021
SWAD: Domain Generalization by Seeking Flat Minima
SWAD: Domain Generalization by Seeking Flat MinimaNeural Information Processing Systems (NeurIPS), 2021
Junbum Cha
Sanghyuk Chun
Kyungjae Lee
Han-Cheol Cho
Seunghyun Park
Yunsung Lee
Sungrae Park
MoMe
751
539
0
17 Feb 2021
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
WGAN with an Infinitely Wide Generator Has No Spurious Stationary PointsInternational Conference on Machine Learning (ICML), 2021
Albert No
Taeho Yoon
Sehyun Kwon
Ernest K. Ryu
GAN
209
2
0
15 Feb 2021
A Survey on Ensemble Learning under the Era of Deep Learning
A Survey on Ensemble Learning under the Era of Deep LearningArtificial Intelligence Review (AIR), 2021
Yongquan Yang
Haijun Lv
Ning Chen
OOD
652
282
0
21 Jan 2021
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial
  Estimation
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial EstimationInternational Conference on Learning Representations (ICLR), 2021
Alexandre Ramé
Matthieu Cord
FedML
244
58
0
14 Jan 2021
Recoding latent sentence representations -- Dynamic gradient-based
  activation modification in RNNs
Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNs
Dennis Ulmer
180
0
0
03 Jan 2021
Topological obstructions in neural networks learning
Topological obstructions in neural networks learning
S. Barannikov
Daria Voronkova
I. Trofimov
Alexander Korotin
Grigorii Sotnikov
Evgeny Burnaev
202
6
0
31 Dec 2020
Recent advances in deep learning theory
Recent advances in deep learning theory
Fengxiang He
Dacheng Tao
AI4CE
332
57
0
20 Dec 2020
Masksembles for Uncertainty Estimation
Masksembles for Uncertainty EstimationComputer Vision and Pattern Recognition (CVPR), 2020
Nikita Durasov
Timur M. Bagautdinov
Pierre Baqué
Pascal Fua
OODUQCV
255
99
0
15 Dec 2020
Notes on Deep Learning Theory
Notes on Deep Learning Theory
Eugene Golikov
VLMAI4CE
90
2
0
10 Dec 2020
Generalization bounds for deep learning
Generalization bounds for deep learning
Guillermo Valle Pérez
A. Louis
BDL
260
48
0
07 Dec 2020
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and
  its Applications to Regularization
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization
Adepu Ravi Sankar
Yash Khasbage
Rahul Vigneswaran
V. Balasubramanian
238
53
0
07 Dec 2020
Encoding the latent posterior of Bayesian Neural Networks for
  uncertainty quantification
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
Gianni Franchi
Andrei Bursuc
Emanuel Aldea
Séverine Dubuisson
Isabelle Bloch
BDLUQCV
319
32
0
04 Dec 2020
Detecting Trojaned DNNs Using Counterfactual Attributions
Detecting Trojaned DNNs Using Counterfactual AttributionsInternational Conference on Applied Algorithms (ICAA), 2020
Karan Sikka
Indranil Sur
Susmit Jha
Anirban Roy
Ajay Divakaran
AAML
158
13
0
03 Dec 2020
SALR: Sharpness-aware Learning Rate Scheduler for Improved
  Generalization
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
Xubo Yue
Maher Nouiehed
Raed Al Kontar
ODL
188
6
0
10 Nov 2020
Efficient and Transferable Adversarial Examples from Bayesian Neural
  Networks
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
Martin Gubri
Maxime Cordy
Mike Papadakis
Yves Le Traon
Koushik Sen
AAML
349
12
0
10 Nov 2020
Numerical Exploration of Training Loss Level-Sets in Deep Neural
  Networks
Numerical Exploration of Training Loss Level-Sets in Deep Neural Networks
Naveed Tahir
Garrett E. Katz
143
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09 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine
  Learning
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander DÁmour
Katherine A. Heller
D. Moldovan
Ben Adlam
B. Alipanahi
...
Kellie Webster
Steve Yadlowsky
T. Yun
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D. Sculley
OffRL
427
764
0
06 Nov 2020
Deep learning versus kernel learning: an empirical study of loss
  landscape geometry and the time evolution of the Neural Tangent Kernel
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent KernelNeural Information Processing Systems (NeurIPS), 2020
Stanislav Fort
Gintare Karolina Dziugaite
Mansheej Paul
Sepideh Kharaghani
Daniel M. Roy
Surya Ganguli
303
219
0
28 Oct 2020
Scalable Bayesian neural networks by layer-wise input augmentation
Scalable Bayesian neural networks by layer-wise input augmentation
Trung Trinh
Samuel Kaski
Markus Heinonen
UQCVBDL
183
3
0
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Decentralized Deep Learning using Momentum-Accelerated Consensus
Decentralized Deep Learning using Momentum-Accelerated Consensus
Aditya Balu
Zhanhong Jiang
Sin Yong Tan
Chinmay Hedge
Young M. Lee
Soumik Sarkar
FedML
207
25
0
21 Oct 2020
Regularizing Neural Networks via Adversarial Model Perturbation
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Yaowei Zheng
Richong Zhang
Yongyi Mao
AAML
415
107
0
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Linear Mode Connectivity in Multitask and Continual Learning
Linear Mode Connectivity in Multitask and Continual LearningInternational Conference on Learning Representations (ICLR), 2020
Seyed Iman Mirzadeh
Mehrdad Farajtabar
Dilan Görür
Razvan Pascanu
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CLL
289
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Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win
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Yani Andrew Ioannou
Cem Keskin
Yann N. Dauphin
207
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