Group theory is extremely successful in characterizing the symmetries quantum systems, which greatly simplifies and unifies our treatments of systems. Here we introduce concept symmetry for a Boltzmann machine develop group to describe symmetry. This implies not only that all target states related with transformations are equivalent, but also given state optimal solutions keep invariant equival...

We present a heuristical procedure for eecient estimation of the partition function in the Boltzmann distribution. The resulting speed-up is of immediate relevance for the speed-up of Boltzmann Machine learning rules, especially for networks with a sparse connectivity.

Dynamic Boltzmann machines (DyBMs) are recently developed generative models of a time series. They are designed to learn a time series by efficient online learning algorithms, whilst taking long-term dependencies into account with help of eligibility traces, recursively updatable memory units storing descriptive statistics of all the past data. The current DyBMs assume a finitedimensional time ...

The idea of Hopfield network is based on the king spin glass model in which each spin has only two possible states: up and down. By introducing stochastic factors into this network and performing a simulated annealing process on it, it becomes a Boltzmann machine which can escape from local minimum states to achieve the global minimum. This paper generalizes the above ideas to multi-value case ...

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got w...

The spike and slab Restricted Boltzmann Machine (ssRBM) is defined by having both a real valued “slab” variable and a binary “spike” variable associated with each unit in the hidden layer. Earlier work exploring a simple incarnation of the ssRBM model family demonstrated its utility as a model of natural images. In this work, we explore an extension of the ssRBM model – the μ-ssRBM – that inclu...

Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. ...

Restricted Boltzmann machines (RBMs) are endowed with the universal power of modeling (binary) joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties (compared with more complicated models, e.g., Boltzmann machines) when dealing with approximation and inference issues. However, in certain computational biology scenarios, such ...