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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.Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent., , , , , , and . NeurIPS, page 8570-8581. (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)4+3 Phases of Compute-Optimal Neural Scaling Laws., , , and . CoRR, (2024)The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks., , , and . NeurIPS, (2020)Finite Versus Infinite Neural Networks: an Empirical Study., , , , , , and . NeurIPS, (2020)Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression., , , , and . NeurIPS, (2022)Disentangling Trainability and Generalization in Deep Neural Networks., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10462-10472. PMLR, (2020)Neural Tangents: Fast and Easy Infinite Neural Networks in Python., , , , , , and . CoRR, (2019)