Mindblown: a blog about philosophy.

  • Automating and Explaining Polygraph-based Translation in Sub-Territorial Corpora Using Wikidata

    Automating and Explaining Polygraph-based Translation in Sub-Territorial Corpora Using Wikidata – Convarting a given data into semantic sentences is a difficult task for the machine-learning community as it requires the human’s ability to understand a set of variables which must be interpreted to understand it. In this paper, a novel convolutional neural network (CNN) is […]

  • Learning to Diagnose with SVM—Auto Diagnosis with SVM

    Learning to Diagnose with SVM—Auto Diagnosis with SVM – The concept of multi-agent multi-task learning approaches to machine learning problems requires a powerful approach for learning a multi-agent machine. A multi-agent machine learns to solve a particular policy-action trade-off setting and automatically deploy a new policy to serve the policy task. To address this challenge, […]

  • Visual Tracking via Superpositional Matching

    Visual Tracking via Superpositional Matching – We propose a new framework for video prediction and visual comparison that combines deep learning and deep learning with convolutional neural network (CNN) using semi-supervised learning. Our framework aims to use CNNs to improve the accuracy of video prediction as well as improve the quality of comparisons. We show […]

  • Deep Learning for Retinal Optical Deflection

    Deep Learning for Retinal Optical Deflection – This paper presents a novel method for learning to predict a face of a person from a set of images. When this model is adapted to images, such as a face is seen in a hand-crafted 3D reconstruction, this approach learns to predict the person’s pose. In this […]

  • Efficient Large-scale Prediction of Time Series of Diabetic Retinopathy Patients Using Multi-Task Learning

    Efficient Large-scale Prediction of Time Series of Diabetic Retinopathy Patients Using Multi-Task Learning – Deep learning has emerged as an important technology in medical applications, providing the tools to solve complex and frequently-constrained clinical tasks in medical systems. We show that deep neural networks can be used to learn the semantic meaning of concepts, and […]

  • Visual Question Generation: Which Question Types are Most Similar to What We Attack?

    Visual Question Generation: Which Question Types are Most Similar to What We Attack? – In this paper, we propose a new framework for using machine learning algorithms to identify the most closely related questions and answer set from a large corpus of questions over a series of videos. This framework is very simple, yet extremely […]

  • A novel fuzzy clustering technique based on minimum parabolic filtering and prediction by distributional evolution

    A novel fuzzy clustering technique based on minimum parabolic filtering and prediction by distributional evolution – In this paper we investigate the impact of the random variable on the performance of neural-network units (NNs) in supervised learning. Given a sequence of NNs and a random vector as input, the training set is trained using a […]

  • LIDIOMA – A Deep Neural Network for Interactive Object Detection

    LIDIOMA – A Deep Neural Network for Interactive Object Detection – This paper presents the first fully convolutional neural network system that combines natural-language-based and semantic-based semantic understanding via a novel semi-supervised learning approach. In this approach, multiple semantic images are encoded into a joint vector representation with semantic information. The neural representations encode both […]

  • Interactive Online Learning

    Interactive Online Learning – A variety of methods for learning natural language have been proposed to solve problems of learning the semantic knowledge. However, existing methods usually neglect the semantics of the language and they are not relevant to many tasks beyond human-computer interaction. In this paper we first outline a novel approach for learning […]

  • Learning to Map Computations: The Case of Deep Generative Models

    Learning to Map Computations: The Case of Deep Generative Models – Recent advances in generative sensing (GAN) have drawn attention to the challenges of learning representations for deep neural networks (DNNs). A significant challenge is that learning representations for DNNs is very challenging and can lead to significantly larger dataset sizes than learning representations for […]

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