3,372 research outputs found
Similarity-based Contrastive Divergence Methods for Energy-based Deep Learning Models
Energy-based deep learning models like Restricted Boltzmann Machines are increasingly used for real-world applications. However, all these models inherently depend on the Contrastive Divergence (CD) method for training and maximization of log likelihood of generating the given data distribution. CD, which internally uses Gibbs sampling, often does not perform well due to issues such as biased samples, poor mixing of Markov chains and highmass probability modes. Variants of CD such as PCD, Fast PCD and Tempered MCMC have been proposed to address this issue. In this work, we propose a new approach to CDbased methods, called Diss-CD, which uses dissimilar data to allow the Markov chain to explore new modes in the probability space. This method can be used with all variants of CD (or PCD), and across all energy-based deep learning models. Our experiments on using this approach on standard datasets including MNIST, Caltech-101 Silhouette and Synthetic Transformations, demonstrate the promise of this approach, showing fast convergence of error in learning and also a better approximation of log likelihood of the data
Efficient Learning for Undirected Topic Models
Replicated Softmax model, a well-known undirected topic model, is powerful in
extracting semantic representations of documents. Traditional learning
strategies such as Contrastive Divergence are very inefficient. This paper
provides a novel estimator to speed up the learning based on Noise Contrastive
Estimate, extended for documents of variant lengths and weighted inputs.
Experiments on two benchmarks show that the new estimator achieves great
learning efficiency and high accuracy on document retrieval and classification.Comment: Accepted by ACL-IJCNLP 2015 short paper. 6 page
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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