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Found 3 papers, 2 papers with code
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations
Considering a probability distribution over parameters is known as an efficient strategy to learn a neural network with non-differentiable activation functions.
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
We present a comprehensive study of multilayer neural networks with binary activation, relying on the PAC-Bayesian theory.
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF) method through the lens of the PAC-Bayesian theory.
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