Training Restricted Boltzmann Machines
نویسندگان
چکیده
منابع مشابه
Training Restricted Boltzmann Machines with Overlapping Partitions
Restricted Boltzmann Machines (RBM) are energy-based models that are successfully used as generative learning models as well as crucial components of Deep Belief Networks (DBN). The most successful training method to date for RBMs is the Contrastive Divergence method. However, Contrastive Divergence is inefficient when the number of features is very high and the mixing rate of the Gibbs chain i...
متن کاملTraining Restricted Boltzmann Machines on Word Observations
The restricted Boltzmann machine (RBM) is a flexible model for complex data. However, using RBMs for high-dimensional multinomial observations poses significant computational difficulties. In natural language processing applications, words are naturally modeled by K-ary discrete distributions, where K is determined by the vocabulary size and can easily be in the hundred thousands. The conventio...
متن کاملEnhanced Gradient for Training Restricted Boltzmann Machines
Restricted Boltzmann machines (RBMs) are often used as building blocks in greedy learning of deep networks. However, training this simple model can be laborious. Traditional learning algorithms often converge only with the right choice of metaparameters that specify, for example, learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data representati...
متن کاملTraining restricted Boltzmann machines: An introduction
Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. They have attracted much attention as building blocks for the multi-layer learning systems called deep belief networks, and variants and extensions of RBMs have found application in a wide range of pattern recognition tasks. This tutorial introduces RBMs from the viewpo...
متن کاملWasserstein Training of Restricted Boltzmann Machines
Boltzmann machines are able to learn highly complex, multimodal, structured and multiscale real-world data distributions. Parameters of the model are usually learned by minimizing the Kullback-Leibler (KL) divergence from training samples to the learned model. We propose in this work a novel approach for Boltzmann machine training which assumes that a meaningful metric between observations is g...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: KI - Künstliche Intelligenz
سال: 2015
ISSN: 0933-1875,1610-1987
DOI: 10.1007/s13218-015-0371-2