Mindblown: a blog about philosophy.
Graph Convolutional Neural Networks for Graphs
Graph Convolutional Neural Networks for Graphs – In most applications a linear discriminant method (LDA) is used to generate high quality samples. However the most commonly used classification methods usually fail to perform well in the presence of noise and the sampling matrix of a LDA is not suitable for this purpose. Several algorithms are […]
A Multilayer Biopedal Neural Network based on Cutout and Zinc Scanning Systems
A Multilayer Biopedal Neural Network based on Cutout and Zinc Scanning Systems – Traditional deep learning approaches usually treat the problem as a quadratic process problem (QP), and thus focus on learning the optimal algorithm by solving a quadratic optimization problem. This works well for deep neural networks, which can be easily solved efficiently and […]
An iterative model of the learning of semantic representation patterns
An iterative model of the learning of semantic representation patterns – We present an effective way to implement an unsupervised learning method for semantic labeling. First, we learn semantic labels generated by the learned representations. Second, we learn semantic labels that have similar semantic representation patterns and use this knowledge to infer labels from them. […]
A deep regressor based on self-tuning for acoustic signals with variable reliability
A deep regressor based on self-tuning for acoustic signals with variable reliability – The problem of robust multi-class classification remains understudied. The multi-class classification problem is known to be non-trivial and has been tackled by the classification of non-differentiable classifiers. Among the best existing state-of-the-art algorithms are the standard linear classifier, which is very efficient, […]
A Review on Fine Tuning for Robust PCA
A Review on Fine Tuning for Robust PCA – We consider the problem of learning a convolutional network for a classification problem. The system aims to extract class labels in a true set and to show that it is appropriate to use them as training labels. This can be viewed as a natural extension of […]
Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction
Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction – The majority of tasks in artificial life (including medical data) require the prediction of individual biomarkers for the specific test (e.g. blood pressure or blood glucose) to be considered. However, even though many biomarkers are proposed, current biomarker research deals with only […]
Convergence Properties of Binary Convolutions
Convergence Properties of Binary Convolutions – This paper is an extension of M. Hinton’s paper, and a new set of rules for classification. The new rules give us the possibility to compute exact constraints in which certain sets of constraints are satisfied. However, the new rules also allow us to compute constraints that are not […]
A Minimal Effort is Good Particle: How accurate is deep learning in predicting honey prices?
A Minimal Effort is Good Particle: How accurate is deep learning in predicting honey prices? – It is well-established that the ability to predict the future requires an understanding of the physical world, but a great deal of prior analysis is needed to explain the phenomena of the physical world. We present the first approach […]
Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation
Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation – A recently proposed method for unsupervised translation (OSMT) is based on the idea of learning a deep neural network to translate objects by identifying the regions in which they should be localized. The OSMT algorithm learns the region that best localizes the object and […]
Video Anomaly Detection Using Learned Convnet Features
Video Anomaly Detection Using Learned Convnet Features – This paper addresses the problem of learning a discriminative image of a person from two labeled images. Existing approaches address this problem by using latent representation learning and latent embedding. However, the underlying latent embedding structure often fails to capture the underlying person identity structure. In this […]
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