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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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06/17/2022
Fast Finite Width Neural Tangent Kernel
The Neural Tangent Kernel (NTK), defined as Θ_θ^f(x_1, x_2) = [∂ f(θ, x_...
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06/15/2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling
We introduce repriorisation, a data-dependent reparameterisation which t...
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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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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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06/18/2020
Exact posterior distributions of wide Bayesian neural networks
Recent work has shown that the prior over functions induced by a deep Ba...
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06/18/2020
Infinite attention: NNGP and NTK for deep attention networks
There is a growing amount of literature on the relationship between wide...
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01/21/2020
On the infinite width limit of neural networks with a standard parameterization
There are currently two parameterizations used to derive fixed kernels c...
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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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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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02/23/2018
Sensitivity and Generalization in Neural Networks: an Empirical Study
In practice it is often found that large over-parameterized neural netwo...
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11/01/2017
Deep Neural Networks as Gaussian Processes
A deep fully-connected neural network with an i.i.d. prior over its para...
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10/20/2016
Iterative Refinement for Machine Translation
Existing machine translation decoding algorithms generate translations i...
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02/23/2016