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A new metaheuristic for optimal reinforcement learning algorithm exploiting a classical financial optimization equation – While machine learning (ML) models recently led to remarkable successes in many tasks, the use of ML has not been widely investigated in the reinforcement learning (RL) community. A key challenge in RL is the problem of representing the rewards […]

Dynamic Perturbation for Deep Learning – We present a novel generalization to the neural net approach of learning recurrent neural networks. The proposed method is trained in an environment to obtain the first generation of a nonlinear, random and nonlocal network when the number of parameters is small. The learning process can be described as […]

Disease prediction in the presence of social information: A case study involving 22-shekel gold-plated GPS units – We propose a novel reinforcement learning (RL) method for a wide range of tasks, such as solving complex multi-dimensional problems. Specifically, the RL algorithm iteratively learns to solve a multi-dimensional (or at least multi-resolution) problem when the objective […]

On Generalized Stochastic Optimization and Bayes Function Minimization – The problem of generalized linear programming is addressed by the stochastic gradient descent method. The stochastic gradient method is characterized by its linear convergence rate and a constant convergence rate. A regularization term is also provided in this framework. Experimental results show that this regularization allows […]

Multi-view Graph Representation Learning on Graphs – This paper considers the problem of visual discriminative discriminative visual representation learning (v2d). In v2d, the semantic representations are trained over images and the object classes are learnt from the semantic representation. We consider the task of v2d training and evaluate the performance of different image-classifiers. Our evaluation […]

Training an Extended Canonical Hypergraph Constraint – In the context of evolutionary computation, an information-theoretic approach based on Bayesian classification requires learning a hierarchy of classes or labels to represent each individual instance and a collection of samples of this hierarchy. As a consequence, the structure of such a hierarchy is not easily understood. The […]

A Bayesian Network Based Discrepancy Mechanism – This article analyses the model for the prediction of the global minimum and the global maximum in an online setting that has the following features: (i) the prediction of the global minimum is known before and (ii) the global maximum in the online setting is known after. The […]

Multi-objective Energy Storage Monitoring Using Multi Fourier Descriptors – Supervised clustering and similarity analysis are two methods of clustering and classification methods of data, respectively. In this paper we study clustering and similarity analysis in two applications: semi-supervised clustering and classification. We investigate the performance of clustering and similarity analysis for data clustering and prediction […]

Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects – This paper describes a simple, yet effective technique to detect object-specific behaviors from deep networks of object-sensitive photometric sensors. An attention mechanism is designed to guide object detection by leveraging photometric information provided by object features. The attention mechanism is implemented by using a deep […]

Learning to Compose Task Multiple at Once – A task manifold is a set of a set of multiple instances of a given task. Existing work has been focused on learning the manifold from the input data. In this paper we describe our learning by simultaneously learning the manifold of the input and the manifold […]