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Machine learning and the physical sciences, , , , , , , and . (2019)cite arxiv:1903.10563.Multi-layer generalized linear estimation., , , and . ISIT, page 2098-2102. IEEE, (2017)Expectation consistency for calibration of neural networks., , , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 443-453. PMLR, (2023)Generalisation error in learning with random features and the hidden manifold model., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 3452-3462. PMLR, (2020)Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 8936-8947. PMLR, (2021)Large deviations for the perceptron model and consequences for active learning., , and . MSML, volume 107 of Proceedings of Machine Learning Research, page 390-430. PMLR, (2020)Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions., , and . NeurIPS, (2020)Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification., , , and . NeurIPS, (2020)Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem., , and . Mach. Learn. Sci. Technol., 2 (3): 35029 (September 2021)Learning curves for the multi-class teacher-student perceptron., , , , , and . Mach. Learn. Sci. Technol., 4 (1): 15019 (March 2023)