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A Hybrid Model for Prediction of Cancer Survivability from Genotypic Changes – A common application of genetic algorithms is the analysis of cancer data from a large number of cells, often in high-dimensional and inhospitable environments. The data is often small, sparse, and has a high risk of non-linearity. This paper presents an algorithm to […]

Segmental Low-Rank Matrix Estimation from Pairwise Similarities via Factorized Matrix Factorization – We describe a method for estimating the semantic similarity between two pairwise similarity data sets by exploiting an inherent dependency structure between the pairwise similarity functions, called {m parametric mappings} (MMF). MMF minimizes the mutual information between a pairwise similarity function and the […]

Learning with Partial Feedback: A Convex Relaxation for Learning with Observational Data – This paper presents a technique for learning to predict and generate large visual representations from multiple sources which are dependent on the environment and user interaction as well as temporal information, and can be used effectively to model the dynamics of various […]

A Novel Feature Selection Methodology for Empirical Science of Electronic Health Records – Recurrent Neural Networks (RNNs) are an exciting new and powerful approach for natural language processing. As the name implies, RNNs encode and represent knowledge transfer. This paper describes a computational framework for neural RNNs that is capable of representing knowledge transfer in […]

On the Existence of a Constraint-Based Algorithm for Learning Regular Expressions – This paper presents a new method to automatically identify a certain kind of dependency and to solve those tasks efficiently. We use the dependency of dependency to compute a sequence of continuous variables that can be used as a source of additional information […]

Learning a graph with all graphs’ connections – This work presents the first step towards a methodology for analyzing the interactions among a set of nodes of a graph. Our approach has focused on the case of two-dimensional and dual graphs. Such as a two-dimensional (2D) graph, a 2D Graph is a graph containing the […]

Learning to Match for Sparse Representation of Images with Convolutional Neural Networks – This paper addresses the problem of image recognition using image compression. The problem involves recovering a compressed image from a low-quality, highly compressed image from intermediate frames. The compression problem stems from the fact that the compressed image contains noise, but a […]

Can natural language processing be extended to the offline domain? – In this paper, we propose a new method for automatic data mining of natural language. Inspired by the work by Farias and Poulard (2017), we develop a supervised machine translation approach which employs a reinforcement learning approach to predict the future of the current […]

Graph-based object detection in high dynamic environment using reinforcement learning – One of the main problems of recent years in robotics has been to solve the problem of robot localization. This has drawn increasing attention in recent years, as the existing approach has been very successful in various applications, such as robotics, biomedical applications, and […]

Learning with Discrete Data for Predictive Modeling – This work presents a novel, unified approach to learn a predictive model with nonlinear constraints. Specifically, we first construct a model in nonlinear context and then perform inference, given the constraints. As opposed to the previous approaches, we perform inference and infer the models, in contrast to […]