We propose the use of competitive learning in deep networks for understanding sequential data. Hierarchies of competitive learning algorithms have been found in the brain [1] and their use in deep vision networks has been validated [2]. The algorithm is simple to comprehend and yet provides fast, sparse learning. To understand temporal patterns we use the depth of the network and delay blocks t...

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and...

The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that such methods must be able to apply in large-scale tasks when dealing with hug...

With the rapid growth and the increasing complexity of network infrastructures and the evolution of attacks, identifying and preventing network abuses is getting more and more strategic to ensure an adequate degree of protection from both external and internal menaces. In this scenario many techniques are emerging for inspecting network traffic and discriminating between anomalous and normal be...

We investigate the problem of estimating the density function of multivariate binary data. In particular, we focus on models for which computing the estimated probability of any data point is tractable. In such a setting, previous work has mostly concentrated on mixture modeling approaches. We argue that for the problem of tractable density estimation, the restricted Boltzmann machine (RBM) pro...

Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state typically used learning to obtain suitable distribution. The consists of calculating gradient loss function given in terms average, which most time consuming procedure. Here, we propose method implement by using Noisy Intermediate-Scale Quantum (NISQ) devices. W...

Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when...