Generative Models For Deep Learning with Very Scarce Data

The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail. We compare the use of two well established techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as generative models in order to increase the training set in a classification framework. Essentially, we rely on Markov Chain Monte Carlo (MCMC) algorithms for generating new samples. We show that generalization can be improved comparing this methodology to other state-of-the-art techniques, e.g. semi-supervised learning with ladder networks. Furthermore, we show that RBM is better than VAE generating new samples for training a classifier with good generalization capabilities

Reply to "Comment on 'Light-Front Schwinger Model at Finite Temperature'"

In hep-th/0310278, Blankleider and Kvinikhidze propose an alternate thermal propagator for the fermions in the light-front Schwinger model. We show that such a propagator does not describe correctly the thermal behavior of fermions in this theory and, as a consequence, the claims made in their paper are not correct.Comment: 3pages, version to be published in Phys. Rev.

Total Cross Sections

A unified approach to total cross-sections, based on the QCD contribution to the rise with energy, is presented for the processes $pp$, $p{\bar p}$, $\gamma p, \gamma \gamma, e^+e^- \to hadrons$. For proton processes, a discussion of the role played by soft gluon summation in taming the fast rise due to mini-jets is presented. For photon-photon processes, a comparison with other models indicates the need for precision measurements in both the low and high energy region, likely only with measurements at future Linear Colliders.Comment: 15 pages, 9 figures, LaTeX, uses hsproc.sty and art10.sty. Talk given by G. Pancheri at 'International Hadron Structure-2000', October 1-6, Staralesn