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pro vyhledávání: '"Anson, Ben"'
Recent work developed convolutional deep kernel machines, achieving 92.7% test accuracy on CIFAR-10 using a ResNet-inspired architecture, which is SOTA for kernel methods. However, this still lags behind neural networks, which easily achieve over 94%
Externí odkaz:
http://arxiv.org/abs/2410.06171
A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed. This is known as a neural network Gaussian process (NNGP). However, the NNGP kerne
Externí odkaz:
http://arxiv.org/abs/2402.06525
Standard infinite-width limits of neural networks sacrifice the ability for intermediate layers to learn representations from data. Recent work (A theory of representation learning gives a deep generalisation of kernel methods, Yang et al. 2023) modi
Externí odkaz:
http://arxiv.org/abs/2309.09814
Deep kernel processes are a recently introduced class of deep Bayesian models that have the flexibility of neural networks, but work entirely with Gram matrices. They operate by alternately sampling a Gram matrix from a distribution over positive sem
Externí odkaz:
http://arxiv.org/abs/2305.14454
Autor:
Yang, Adam X., Robeyns, Maxime, Milsom, Edward, Anson, Ben, Schoots, Nandi, Aitchison, Laurence
The successes of modern deep machine learning methods are founded on their ability to transform inputs across multiple layers to build good high-level representations. It is therefore critical to understand this process of representation learning. Ho
Externí odkaz:
http://arxiv.org/abs/2108.13097
Autor:
Anson, Ben
Publikováno v:
Four Four Two; Feb2017, Issue 272, p27-27, 5/8p, 1 Color Photograph
Autor:
Anson, Ben
Publikováno v:
Living Woods. Mar/Apr2014, Issue 33, p36-37. 2p.
Autor:
Anson, Ben
Publikováno v:
Living Woods. Sep/Oct2013, Issue 30, p32-33. 2p.