Neural Simpletrons - Minimalistic Directed Generative Networks for Learning with Few Labels

28 June 2015

Papers citing "Neural Simpletrons - Minimalistic Directed Generative Networks for Learning with Few Labels"

6 / 6 papers shown

Title
Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters Sebastian Salwig Till Kahlke F. Hirschberger D. Forster Jorg Lucke VLM 89 0 0 21 Jan 2025
An Overview of Deep Semi-Supervised Learning Yassine Ouali C´eline Hudelot Myriam Tami SSL HAI 27 294 0 09 Jun 2020
Regularization by architecture: A deep prior approach for inverse problems Sören Dittmer T. Kluth Peter Maass Daniel Otero Baguer 35 97 0 10 Dec 2018
$k$ -means as a variational EM approximation of Gaussian mixture models Jörg Lücke D. Forster DRL VLM 13 49 0 16 Apr 2017
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 213 745 0 06 Jun 2015
Modeling Documents with Deep Boltzmann Machines Nitish Srivastava Ruslan Salakhutdinov Geoffrey E. Hinton BDL 88 184 0 26 Sep 2013