Speaker
Stefano Carrazza
(CERN)
Description
We present a novel general Boltzmann machine with continuous visible
and discrete integer valued hidden states, yielding a parametric
density function involving a ratio of Riemann-Theta functions. After a
brief overview of the theory required to define this new ML
architecture, we show how the conditional expectation of a hidden
state for given visible states can be used as activation function in a
feedforward neural network, thereby increasing the modeling capacity
of the network. We then provide application examples for density
estimation, data regression and data classification in HEP. This work
is based on arXiv:1712.07581 and arXiv:1804.07768.
Primary author
Stefano Carrazza
(CERN)
