A statistician teaches deep learning
From MaRDI portal
Publication:2241468
DOI10.1007/s42519-021-00193-0OpenAlexW3136900221MaRDI QIDQ2241468
Publication date: 9 November 2021
Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2102.01194
Uses Software
Cites Work
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- Multilayer feedforward networks are universal approximators
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- High-dimensional dynamics of generalization error in neural networks
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Adversarial classification: an adversarial risk analysis approach
- Rademacher penalties and structural risk minimization
- 10.1162/153244303321897690
- Reconciling modern machine-learning practice and the classical bias–variance trade-off
- Understanding Machine Learning
- A Stochastic Approximation Method
- Wide neural networks of any depth evolve as linear models under gradient descent *
- A jamming transition from under- to over-parametrization affects generalization in deep learning
- Approximation by superpositions of a sigmoidal function
- Model selection and error estimation