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Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit.

, , , , and . ICLR, OpenReview.net, (2021)

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Disentangling trainability and generalization in deep learning., , and . CoRR, (2019)Neural Tangents: Fast and Easy Infinite Neural Networks in Python., , , , , , and . ICLR, OpenReview.net, (2020)Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes, , , , , , , , and . (2018)cite arxiv:1810.05148Comment: Published as a conference paper at ICLR 2019.Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?, , , , , , , , , and 20 other author(s). CoRR, (2023)Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent., , , , , , and . NeurIPS, page 8570-8581. (2019)Small-scale proxies for large-scale Transformer training instabilities., , , , , , , , , and 6 other author(s). CoRR, (2023)Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent., , , , , and . CoRR, (2019)Disentangling Trainability and Generalization in Deep Neural Networks., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10462-10472. PMLR, (2020)Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression., , , , and . NeurIPS, (2022)The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks., , , and . NeurIPS, (2020)