# Understanding deep learning (still) requires rethinking generalization

@article{Zhang2021UnderstandingDL, title={Understanding deep learning (still) requires rethinking generalization}, author={Chiyuan Zhang and Samy Bengio and Moritz Hardt and Benjamin Recht and Oriol Vinyals}, journal={Communications of the ACM}, year={2021}, volume={64}, pages={107 - 115} }

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establishâ€¦Â

## 111 Citations

Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping

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It is demonstrated that there exist learning problems where natural gradient descent fails to generalize, while gradient descent with the right architecture performs well.

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- Computer Science, PhysicsArXiv
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It is argued that a future deep learning theory should inherit three characteristics: a hierarchically structured network architecture, parameters iteratively optimized using stochastic gradient-based methods, and information from the data that evolves compressively.

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A compression-based framework is established that is simple and powerful enough to extend the generalization bounds by Arora et al. to also hold for the original network and allows for simple proofs of the strongest known generalization limits for other popular machine learning models, namely Support Vector Machines and Boosting.

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- Computer Science, MathematicsArXiv
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