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09/09/2022
Fast Neural Kernel Embeddings for General Activations
Infinite width limit has shed light on generalization and optimization a...
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07/11/2022
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm
Although learning in high dimensions is commonly believed to suffer from...
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05/30/2022
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression
As modern machine learning models continue to advance the computational ...
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12/10/2021
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks
Understanding the fundamental principles behind the massive success of n...
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07/27/2021
Dataset Distillation with Infinitely Wide Convolutional Networks
The effectiveness of machine learning algorithms arises from being able ...
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10/14/2020
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Modern deep learning models have achieved great success in predictive ac...
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07/31/2020
Finite Versus Infinite Neural Networks: an Empirical Study
We perform a careful, thorough, and large scale empirical study of the c...
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01/16/2020
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
The selection of initial parameter values for gradient-based optimizatio...
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12/30/2019
Disentangling trainability and generalization in deep learning
A fundamental goal in deep learning is the characterization of trainabil...
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12/05/2019
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a library designed to enable research into infinite-w...
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02/18/2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
A longstanding goal in deep learning research has been to precisely char...
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10/11/2018
Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes
There is a previously identified equivalence between wide fully connecte...
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06/14/2018