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Recent works have suggested that finite Bayesian neural networks may sometimes outperform their infinite cousins because finite networks can flexibly adapt their internal representations.
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Leon Isserlis · 1918
Earlier work this paper cites.
The evaluation of the collision matrix
Gian-Carlo Wick · 1950
Earlier work this paper cites.
A practical Bayesian framework for backpropagation networks
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Peter E Kloeden and Eckhard Platen · 1992
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Radford M Neal · 1996
Earlier work this paper cites.
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Christopher KI Williams · 1997
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Earlier work this paper cites.
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Feature learning in infinite-width neural networks
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Predicting the outputs of finite networks trained with noisy gradients
Gadi Naveh, Oded Ben-David, Haim Sompolinsky, and Zohar Ringel · 2020
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Asymptotics of wide networks from Feynman diagrams
Ethan Dyer and Guy Gur-Ari · 2020
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On the asymptotics of wide networks with polynomial activations
Kyle Aitken and Guy Gur-Ari · 2020
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Exact marginal prior distributions of finite Bayesian neural networks
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Bayesian neural network priors revisited
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Random neural networks in the infinite width limit as Gaussian processes
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A self consistent theory of Gaussian processes captures feature learning effects in finite CNNs
Gadi Naveh and Zohar Ringel · 2021
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Precise characterization of the prior predictive distribution of deep ReLU networks
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