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Averaging Weights Leads to Wider Optima and Better Generalization
v1v2v3 (latest)

Averaging Weights Leads to Wider Optima and Better Generalization

14 March 2018
Pavel Izmailov
Dmitrii Podoprikhin
T. Garipov
Dmitry Vetrov
A. Wilson
    FedMLMoMe
ArXiv (abs)PDFHTML

Papers citing "Averaging Weights Leads to Wider Optima and Better Generalization"

50 / 1,040 papers shown
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Humble Teachers Teach Better Students for Semi-Supervised Object
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Weifeng Chen
Yijun Luo
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0
19 Jun 2021
Effective Evaluation of Deep Active Learning on Image Classification
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D. Sivasubramanian
Apurva Dani
Ganesh Ramakrishnan
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39
0
16 Jun 2021
ScheduleNet: Learn to solve multi-agent scheduling problems with
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Sanjar Bakhtiyar
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NODE-GAM: Neural Generalized Additive Model for Interpretable Deep
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NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
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Anna Goldenberg
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SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
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Boyuan Zheng
Xiaoyu Yang
Yu-Ping Ruan
Zhen-Hua Ling
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Informing Geometric Deep Learning with Electronic Interactions to
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Efficient and Accurate Gradients for Neural SDEs
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Calibration and Uncertainty Quantification of Bayesian Convolutional
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Visual FUDGE: Form Understanding via Dynamic Graph Editing
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Chris Tensmeyer
Curtis Wigington
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SelfReg: Self-supervised Contrastive Regularization for Domain
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Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in
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Robustness to Pruning Predicts Generalization in Deep Neural Networks
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