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The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time.
Generalization in a linear perceptron in the presence of noise
Anders Krogh and John A Hertz · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Artificial intelligence: an empirical science
Herbert A Simon · 1995
Earlier work this paper cites.
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Some pac-bayesian theorems
David A McAllester · 1998
Earlier work this paper cites.
Information theory, inference and learning algorithms
David JC MacKay · 2003
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Earlier work this paper cites.
Gintare Karolina Dziugaite and Daniel M Roy · 2017
Earlier work this paper cites.
A bayesian perspective on generalization and stochastic gradient descent
Samuel L Smith and Quoc V Le · 2017
Earlier work this paper cites.
Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
Earlier work this paper cites.
Non-vacuous generalization bounds at the imagenet scale: a pac-bayesian compression approach
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P Adams, and Peter Orbanz · 2018
Earlier work this paper cites.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Cited alongside, same era.
On the measure of intelligence
François Chollet · 2019
Cited alongside, same era.
Practical deep learning with bayesian principles
Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz E Khan, Anirudh Jain, Runa Eschenhagen, Richard E Turner, and Rio Yokota · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Laplace redux-effortless bayesian deep learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, and Philipp Hennig · 2021
Later among the works it cites.
Stochastic training is not necessary for generalization
Jonas Geiping, Micah Goldblum, Phillip E Pope, Michael Moeller, and Tom Goldstein · 2021
Later among the works it cites.
Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
Later among the works it cites.
Towards an empirical theory of deep learning
Preetum Nakkiran · 2021
Later among the works it cites.
Loss landscapes are all you need: Neural network generalization can be explained without the implicit bias of gradient descent
Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, and Tom Goldstein · 2022
Later among the works it cites.
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Understanding generalization through visualizations
W Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, JK Terry, Furong Huang, and Tom Goldstein · 2019
Cited alongside, same era.
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
Cited alongside, same era.
Botorch: A framework for efficient monte-carlo bayesian optimization
Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wilson, and Eytan Bakshy · 2020
Cited alongside, same era.
Rethinking parameter counting in deep models: Effective dimensionality revisited
Wesley J Maddox, Gregory Benton, and Andrew Gordon Wilson · 2020
Cited alongside, same era.
Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis · 2020
Cited alongside, same era.
Bayesian deep learning and a probabilistic perspective of generalization
Andrew G Wilson and Pavel Izmailov · 2020
Cited alongside, same era.
User-friendly introduction to pac-bayes bounds
Pierre Alquier · 2021
Cited alongside, same era.
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, and Andrew Gordon Wilson · 2022
Later among the works it cites.
Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
Later among the works it cites.
Pac-bayes compression bounds so tight that they can explain generalization
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, and Andrew G Wilson · 2022
Later among the works it cites.
The Abstraction and Reasoning Corpus (ARC)
F. Chollet · 2023
Closest in time.
Baby steps in evaluating the capacities of large language models
Michael C Frank · 2023
Closest in time.
Micah Goldblum, Marc Finzi, Keefer Rowan, and Andrew Gordon Wilson · 2023
Closest in time.
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Andres Potapczynski, Marc Finzi, Geoff Pleiss, and Andrew Gordon Wilson · 2023
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