A Brief Tour of Deep Learning from a Statistical Perspective
| Authors |
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| Publication date | 2023 |
| Journal | Annual Review of Statistics and Its Application |
| Volume | Issue number | 10 |
| Pages (from-to) | 219-246 |
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| Abstract | We expose the statistical foundations of deep learning with the goal of facilitating conversation between the deep learning and statistics communities. We highlight core themes at the intersection; summarize key neural models, such as feedforward neural networks, sequential neural networks, and neural latent variable models; and link these ideas to their roots in probability and statistics. We also highlight research directions in deep learning where there are opportunities for statistical contributions. |
| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1146/ANNUREV-STATISTICS-032921-013738 |
| Downloads |
annurev-statistics-032921-013738
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