Saurabh Paul, Christos Boutsidis, et al.
JMLR
We classify points in Rd (feature vectors) by func- tions related to feedforward artificial neural networks (ANNs). These functions, dubbed “stochastic neural nets,” arise in a natural way from probabilistic as well as statistical considerations. The probabilistic idea is to define a classifying bit locally by using the sign of a hidden state-dependent noisy linear function of the feature vector as a new d + 1st coordinate of the vector. This d + 1-dimensional distribution is approximated by a mixture distribution. The statistical idea is that the approximating mixtures, and hence the a posteriori class probability functions (stochastic neural nets) defined by them, can be conveniently trained either by maximum likelihood or by a Bayes criterion through the use of an appropriate Expectation-Maximization (EM) algorithm. © 1995 IEEE
Saurabh Paul, Christos Boutsidis, et al.
JMLR
Arnold.L. Rosenberg
Journal of the ACM
Matteo Baldoni, Nirmit Desai, et al.
AAMAS 2009
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023