Topic Tag: Boltzmann Machine

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 Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond

For Markov chain Monte Carlo methods, one of the greatest discrepancies between theory and system is the scan order – while most theoretical development on the mixing time analysis deals with random updates, real-world systems are implemented with systematic scans. We bridge this gap for mode…


 A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines

Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures. In this work, we derive a deterministic framework for the training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer (…


 A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks

We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used non…


 Differentially Private Mixture of Generative Neural Networks

  

Over the past few years, an increasing number of applications of generative models have emerged that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations of individuals whose data is used to train these models, however, publishing or sharing…


 On better training the infinite restricted Boltzmann machines

  

The infinite restricted Boltzmann machine (iRBM) is an extension of the classic RBM. It enjoys a good property of automatically deciding the size of the hidden layer according to specific training data. With sufficient training, the iRBM can achieve a competitive performance with that of the classi…


 Phase transitions in Restricted Boltzmann Machines with generic priors

We study Generalised Restricted Boltzmann Machines with generic priors for units and weights, interpolating between Boolean and Gaussian variables. We present a complete analysis of the replica symmetric phase diagram of these systems, which can be regarded as Generalised Hopfield models. We underl…


 Unsupervised Generative Modeling Using Matrix Product States

  

Generative modeling, which learns joint probability distribution from training data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix pr…


 Boltzmann machines for time-series

 

We review Boltzmann machines extended for time-series. These models often have recurrent structure, and back propagration through time (BPTT) is used to learn their parameters. The per-step computational complexity of BPTT in online learning, however, grows linearly with respect to the length of pr…


 Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints

 

We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient descent constraint optimization to provide further control over the generation process. Among other things, …