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We consider fine-tuning a pretrained deep neural network on a target task.
“PAC-Bayesian model averaging”
David McAllester · 1999
Earlier work this paper cites.
“Some pac-bayesian theorems”
David McAllester · 1999
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“PAC-Bayesian model averaging”
David McAllester · 1999
Earlier work this paper cites.
“Some pac-bayesian theorems”
David McAllester · 1999
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“PAC generalization bounds for co-training”
Sanjoy Dasgupta, Michael Littman and David McAllester · 2001
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“PAC generalization bounds for co-training”
Sanjoy Dasgupta, Michael Littman and David McAllester · 2001
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“On early stopping in gradient descent learning”
Yuan Yao, Lorenzo Rosasco and Andrea Caponnetto · 2007
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“On early stopping in gradient descent learning”
Yuan Yao, Lorenzo Rosasco and Andrea Caponnetto · 2007
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“A notion of task relatedness yielding provable multiple-task learning guarantees”
Shai Ben-David and Reba Borbely · 2008
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“Learning from Multiple Sources”
Koby Crammer, Michael Kearns and Jennifer Wortman · 2008
Earlier work this paper cites.
“A notion of task relatedness yielding provable multiple-task learning guarantees”
Shai Ben-David and Reba Borbely · 2008
Earlier work this paper cites.
“Learning from Multiple Sources”
Koby Crammer, Michael Kearns and Jennifer Wortman · 2008
Earlier work this paper cites.
“A theory of learning from different domains”
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira and Jennifer Vaughan · 2010
Earlier work this paper cites.
“A theory of learning from different domains”
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira and Jennifer Vaughan · 2010
Earlier work this paper cites.
“A PAC-Bayesian tutorial with a dropout bound”
David McAllester · 2013
Earlier work this paper cites.
“Learning with noisy labels”
Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar and Ambuj Tewari · 2013
Earlier work this paper cites.
“A PAC-Bayesian tutorial with a dropout bound”
David McAllester · 2013
Earlier work this paper cites.
“Learning with noisy labels”
Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar and Ambuj Tewari · 2013
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“Classification with noisy labels by importance reweighting”
Tongliang Liu and Dacheng Tao · 2015
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“Classification with noisy labels by importance reweighting”
Tongliang Liu and Dacheng Tao · 2015
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“Eigenvalues of the hessian in deep learning: Singularity and beyond”
Levent Sagun, Leon Bottou and Yann LeCun · 2016
Earlier work this paper cites.
“Eigenvalues of the hessian in deep learning: Singularity and beyond”
Levent Sagun, Leon Bottou and Yann LeCun · 2016
Earlier work this paper cites.
“Spectrally-normalized margin bounds for neural networks”
Peter Bartlett, Dylan Foster and Matus Telgarsky · 2017
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“Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data”
Gintare Dziugaite and Daniel Roy · 2017
Earlier work this paper cites.
“Cost-Sensitive Learning with Noisy Labels.”
Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar and Ambuj Tewari · 2017
Earlier work this paper cites.
“Making deep neural networks robust to label noise: A loss correction approach”
Giorgio Patrini, Alessandro Rozza, Aditya Krishna, Richard Nock and Lizhen Qu · 2017
Earlier work this paper cites.
“Pac-bayesian margin bounds for convolutional neural networks”
Konstantinos Pitas, Mike Davies and Pierre Vandergheynst · 2017
Earlier work this paper cites.
“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2017
Earlier work this paper cites.
“Spectrally-normalized margin bounds for neural networks”
Peter Bartlett, Dylan Foster and Matus Telgarsky · 2017
Earlier work this paper cites.
“Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data”
Gintare Dziugaite and Daniel Roy · 2017
Earlier work this paper cites.
“Cost-Sensitive Learning with Noisy Labels.”
Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar and Ambuj Tewari · 2017
Earlier work this paper cites.
“Making deep neural networks robust to label noise: A loss correction approach”
Giorgio Patrini, Alessandro Rozza, Aditya Krishna, Richard Nock and Lizhen Qu · 2017
Earlier work this paper cites.
“Pac-bayesian margin bounds for convolutional neural networks”
Konstantinos Pitas, Mike Davies and Pierre Vandergheynst · 2017
Earlier work this paper cites.
“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2017
Earlier work this paper cites.
“Stronger generalization bounds for deep nets via a compression approach”
Sanjeev Arora, Rong Ge, Behnam Neyshabur and Yi Zhang · 2018
Earlier work this paper cites.
“Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience”
Vaishnavh Nagarajan and Zico Kolter · 2018
Earlier work this paper cites.
“A pac-bayesian approach to spectrally-normalized margin bounds for neural networks”
Behnam Neyshabur, Srinadh Bhojanapalli and Nathan Srebro · 2018
Earlier work this paper cites.
“The full spectrum of deepnet hessians at scale: Dynamics with sgd training and sample size”
Vardan Papyan · 2018
Earlier work this paper cites.
“Stronger generalization bounds for deep nets via a compression approach”
Sanjeev Arora, Rong Ge, Behnam Neyshabur and Yi Zhang · 2018
Earlier work this paper cites.
“Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience”
Vaishnavh Nagarajan and Zico Kolter · 2018
Cited alongside, same era.
“A pac-bayesian approach to spectrally-normalized margin bounds for neural networks”
Behnam Neyshabur, Srinadh Bhojanapalli and Nathan Srebro · 2018
Cited alongside, same era.
“The full spectrum of deepnet hessians at scale: Dynamics with sgd training and sample size”
Vardan Papyan · 2018
Cited alongside, same era.
“Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks”
Peter Bartlett, Nick Harvey, Christopher Liaw and Abbas Mehrabian · 2019
Cited alongside, same era.
“An investigation into neural net optimization via hessian eigenvalue density”
Behrooz Ghorbani, Shankar Krishnan and Ying Xiao · 2019
Cited alongside, same era.
“Fixmatch: Simplifying semi-supervised learning with consistency and confidence”
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin Cubuk, Alex Kurakin, Han Zhang and Colin Raffel · 2020
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“On the theory of transfer learning: The importance of task diversity”
Nilesh Tripuraneni, Michael Jordan and Chi Jin · 2020
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“Normalized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analysis”
Yusuke Tsuzuku, Issei Sato and Masashi Sugiyama · 2020
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“Improved sample complexities for deep neural networks and robust classification via an all-layer margin”
Colin Wei and Tengyu Ma · 2020
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“Understanding and improving information transfer in multi-task learning”
Sen Wu, Hongyang Zhang and Christopher Ré · 2020
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“A Primer on PAC-Bayesian Learning”
Benjamin Guedj · 2019
Cited alongside, same era.
“Generalization in deep networks: The role of distance from initialization”
Vaishnavh Nagarajan and J Kolter · 2019
Cited alongside, same era.
“Towards understanding the role of over-parametrization in generalization of neural networks”
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun and Nathan Srebro · 2019
Cited alongside, same era.
“Measurements of three-level hierarchical structure in the outliers in the spectrum of deepnet hessians”
Vardan Papyan · 2019
Cited alongside, same era.
“High-dimensional statistics: A non-asymptotic viewpoint”
Martin Wainwright · 2019
Cited alongside, same era.
“Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks”
Peter Bartlett, Nick Harvey, Christopher Liaw and Abbas Mehrabian · 2019
Cited alongside, same era.
“An investigation into neural net optimization via hessian eigenvalue density”
Behrooz Ghorbani, Shankar Krishnan and Ying Xiao · 2019
Cited alongside, same era.
“On the generalization effects of linear transformations in data augmentation”
Sen Wu, Hongyang Zhang, Gregory Valiant and Christopher Ré · 2020
Later among the works it cites.
“Dissecting hessian: Understanding common structure of hessian in neural networks”
Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie Wang and Rong Ge · 2020
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“Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers”
Fan Yang, Hongyang Zhang, Sen Wu, Christopher Ré and Weijie Su · 2020
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“Dual t: Reducing estimation error for transition matrix in label-noise learning”
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu and Masashi Sugiyama · 2020
Later among the works it cites.
“Pyhessian: Neural networks through the lens of the hessian”
Zhewei Yao, Amir Gholami, Kurt Keutzer and Michael Mahoney · 2020
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“Technical perspective: Why don’t today’s deep nets overfit to their training data?”
Sanjeev Arora · 2021
Later among the works it cites.
“Learning Augmentation Distributions using Transformed Risk Minimization”
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban and Kostas Daniilidis · 2021
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“Label noise sgd provably prefers flat global minimizers”
Alex Damian, Tengyu Ma and Jason Lee · 2021
Later among the works it cites.
“On the role of data in PAC-Bayes”
Gintare Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino and Daniel Roy · 2021
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“Sharpness-aware minimization for efficiently improving generalization”
Pierre Foret, Ariel Kleiner, Hossein Mobahi and Behnam Neyshabur · 2021
Later among the works it cites.
“Distance-Based Regularisation of Deep Networks for Fine-Tuning”
Henry Gouk, Timothy Hospedales and Massimiliano Pontil · 2021
Later among the works it cites.
“Patterns, predictions, and actions: A story about machine learning”
Moritz Hardt and Benjamin Recht · 2021
Later among the works it cites.
“Near-optimal linear regression under distribution shift”
Qi Lei, Wei Hu and Jason Lee · 2021
Later among the works it cites.
“Improved Regularization and Robustness for Fine-tuning in Neural Networks”
Dongyue Li and Hongyang Zhang · 2021
Later among the works it cites.
“A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks”
Renjie Liao, Raquel Urtasun and Richard Zemel · 2021
Later among the works it cites.
“Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees”
Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen Bach and Eli Upfal · 2021
Later among the works it cites.
“A Theoretical Analysis of Fine-tuning with Linear Teachers”
Gal Shachaf, Alon Brutzkus and Amir Globerson · 2021
Later among the works it cites.
“Technical perspective: Why don’t today’s deep nets overfit to their training data?”
Sanjeev Arora · 2021
Later among the works it cites.
“Learning Augmentation Distributions using Transformed Risk Minimization”
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban and Kostas Daniilidis · 2021
Later among the works it cites.
“Label noise sgd provably prefers flat global minimizers”
Alex Damian, Tengyu Ma and Jason Lee · 2021
Later among the works it cites.
“On the role of data in PAC-Bayes”
Gintare Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino and Daniel Roy · 2021
Later among the works it cites.
“Sharpness-aware minimization for efficiently improving generalization”
Pierre Foret, Ariel Kleiner, Hossein Mobahi and Behnam Neyshabur · 2021
Later among the works it cites.
“Distance-Based Regularisation of Deep Networks for Fine-Tuning”
Henry Gouk, Timothy Hospedales and Massimiliano Pontil · 2021
Later among the works it cites.
“Patterns, predictions, and actions: A story about machine learning”
Moritz Hardt and Benjamin Recht · 2021
Later among the works it cites.
“Near-optimal linear regression under distribution shift”
Qi Lei, Wei Hu and Jason Lee · 2021
Later among the works it cites.
“Improved Regularization and Robustness for Fine-tuning in Neural Networks”
Dongyue Li and Hongyang Zhang · 2021
Later among the works it cites.
“A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks”
Renjie Liao, Raquel Urtasun and Richard Zemel · 2021
Later among the works it cites.
“Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees”
Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen Bach and Eli Upfal · 2021
Later among the works it cites.
“A Theoretical Analysis of Fine-tuning with Linear Teachers”
Gal Shachaf, Alon Brutzkus and Amir Globerson · 2021
Later among the works it cites.
“Does the data induce capacity control in deep learning?”
Rubing Yang, Jialin Mao and Pratik Chaudhari · 2022
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“Does the data induce capacity control in deep learning?”
Rubing Yang, Jialin Mao and Pratik Chaudhari · 2022
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“Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion”
Haotian Ju, Dongyue Li, Aneesh Sharma and Hongyang Zhang · 2023
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“Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion”
Haotian Ju, Dongyue Li, Aneesh Sharma and Hongyang Zhang · 2023
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