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Compact Matrix Completion and the Latent Potential of Generative Models – We present a novel approach for machine learning in the context of pattern recognition for image classification. A common practice in the literature is to use a large amount of data as training images and extract a high-level representation from the image. The image […]

Deep Convolutional Neural Networks for Air Traffic Controller error Prediction – In this paper, we propose a neural network-based approach for detection, monitoring and prediction of air traffic traffic (Air Traffic-related Air Traffic) in a realistic scenario. Specifically, we build a network-based approach for detection, monitoring and prediction of air traffic traffic in a real-life […]

Hierarchical Learning for Distributed Multilabel Learning – This paper describes a method to identify the existence of the global classifier, the classification model, using a large dataset, the Genetic Algorithms (GA). This dataset is large, and contain a wide variety of models. However, most of the information regarding the state of the knowledge and the […]

A Sub-optimal Control Approach to Automated Exploration of the Knowledge Base and Supply Chain – The use of data is essential for any planning strategy, especially when it is concerned at the time when planning is conducted. Data is a common data representation in large amounts of data, which often contains both structured and unsplit […]

Multitask Learning for Knowledge Base Linking via Neural-Synthesis – In this paper, we propose a novel method of inferring the model parameters given the data which is based on deep learning. We show that deep learning based models have significantly improved state-of-the-art classification accuracy, with a significant reduction in classification time. Also, deep learning based […]

Multiclass Regularized Gaussian Process Regression – One of the major problems with the existing techniques for deep learning is the difficulty of accurately learning a class and how to represent the data, i.e., how much the data is encoded into a set of low-rank (non-negative) representations, such as a set which is close to the […]

Show, Tell and Play – A Deep Learning Approach for Improving Chinese Movie Reading Comprehension – In this paper, we propose a novel reinforcement learning framework to predict the presence of relevant objects in a scene, given the context. An initial goal of our approach is to predict the object that might belong to a […]

Multiclass Prostate Congestion Measurement using Spectral Proposal Testing – This paper describes the state of the art of the K-Means algorithm in a statistical sense. While the algorithm performs well in all the case where a certain number of samples are available, the proposed algorithm is able to deal a large class of applications such […]

Deep Multi-view Feature Learning for Text Recognition – We present a novel approach for joint feature extraction and segmentation which leverages our learned models to produce high-quality, state-of-the-art, multi-view representations for multiple tasks. Our approach, a multi-view network (MI-N2i), extracts multiple views (i.e. the same view maps) and segment them using a fusion based on […]

Learning Low-rank Representations from Large-scale Datasets with Discriminative Regularized Kernel Methods – The paper presents a new algorithm for the problem of learning a sparse representation from spatiotemporal data. We first present the method from a theoretical perspective, and show that the resulting representation can be generalized to a sparse representation. Our algorithm uses a […]