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Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

, , , , , , , , and . (2018)cite arxiv:1810.05148Comment: Published as a conference paper at ICLR 2019.

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Neural Tangents: Fast and Easy Infinite Neural Networks in Python., , , , , , and . ICLR, OpenReview.net, (2020)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)Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes, , , , , , , , and . (2018)cite arxiv:1810.05148Comment: Published as a conference paper at ICLR 2019.4+3 Phases of Compute-Optimal Neural Scaling Laws., , , and . CoRR, (2024)Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent., , , , , , and . NeurIPS, page 8570-8581. (2019)Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes., , , , , , , , and . ICLR (Poster), OpenReview.net, (2019)Neural Tangents: Fast and Easy Infinite Neural Networks in Python., , , , , , and . CoRR, (2019)Dataset Distillation with Infinitely Wide Convolutional Networks., , , and . NeurIPS, page 5186-5198. (2021)Eigenspace Restructuring: A Principle of Space and Frequency in Neural Networks.. COLT, volume 178 of Proceedings of Machine Learning Research, page 4888-4944. PMLR, (2022)Fast Neural Kernel Embeddings for General Activations., , , , , and . NeurIPS, (2022)