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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.01592 |
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| _version_ | 1866909104462626816 |
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| author | Bahri, Yasaman Hanin, Boris Brossollet, Antonin Erba, Vittorio Keup, Christian Pacelli, Rosalba Simon, James B. |
| author_facet | Bahri, Yasaman Hanin, Boris Brossollet, Antonin Erba, Vittorio Keup, Christian Pacelli, Rosalba Simon, James B. |
| contents | These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks; connections between trained deep neural networks, linear models, kernels, and Gaussian processes that arise in the infinite-width limit; and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_01592 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Les Houches Lectures on Deep Learning at Large & Infinite Width Bahri, Yasaman Hanin, Boris Brossollet, Antonin Erba, Vittorio Keup, Christian Pacelli, Rosalba Simon, James B. Machine Learning Artificial Intelligence High Energy Physics - Theory Probability These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks; connections between trained deep neural networks, linear models, kernels, and Gaussian processes that arise in the infinite-width limit; and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training. |
| title | Les Houches Lectures on Deep Learning at Large & Infinite Width |
| topic | Machine Learning Artificial Intelligence High Energy Physics - Theory Probability |
| url | https://arxiv.org/abs/2309.01592 |