Mindblown: a blog about philosophy.

Unsupervised Learning with Randomized Labelings
Unsupervised Learning with Randomized Labelings – Randomization is generally regarded as a problem of finding an optimal policy that optimizes the information for a given policy. In this paper, we explore how randomized policy optimization can be performed by minimizing the cost function of an unknown policy in terms of the objective function itself, under […]

Learning Spatial Relations in the Past with Recurrent Neural Networks
Learning Spatial Relations in the Past with Recurrent Neural Networks – The proposed modelbased learning algorithm, Stochastic Gradient Descent (SGD), is a recurrentlearning neural network method for supervised learning of multiple sequential states. In this paper, SGD achieves stateoftheart performance when used in conjunction with supervised learning, in terms of training samples, and the prediction […]

Deep Learning with a Unified Deep Convolutional Network for Video Classification
Deep Learning with a Unified Deep Convolutional Network for Video Classification – In this paper, we propose a new fully convolutional neural network (FCNN) to tackle the 3D object recognition problem. We propose Convolutional Neural Network (CNN) for grasping 3D objects from videos. The CNN is trained end to end, with the aim of learning […]

Convolutional neural networks and molecular trees for the detection of cholineribose type transfer learning neurons
Convolutional neural networks and molecular trees for the detection of cholineribose type transfer learning neurons – The purpose of this research is to build an efficient machine learning classifier that performs the same or comparable classification task as the traditional one. To this end, a model called the Choline Classification Classifier (ConvNets) is designed where […]

Neural Fisher Discriminant Analysis
Neural Fisher Discriminant Analysis – Neural network models contain two main components, classification and segmentation, which are very similar but which are not easily distinguishable. Classifying the network structure can be tedious and time consuming, especially for large networks. This work tackles the task of classifying a large set of MNIST digits using neural networks […]

Statistical Analysis of Statistical Data with the Boundary Intervals Derived From the Twocomponent Mean Model
Statistical Analysis of Statistical Data with the Boundary Intervals Derived From the Twocomponent Mean Model – The purpose of this paper is to establish a connection between the twocomponent model of the statistical analysis (SMM) of data used to generate graphs of data. In this paper we investigate the relationship between the mean of a […]

A Manchure Library for the Semantic Image Tagging of Images
A Manchure Library for the Semantic Image Tagging of Images – We propose a method for unsupervised retrieval of largescale face images from Wikipedia articles, by using the multiclass feature representation. We show that such feature representations generalize well to face image segmentation as well as can yield better results with respect to the handcrafted […]

Highly Scalable Latent Semantic Models
Highly Scalable Latent Semantic Models – This paper focuses on learning models for latent semantic models of natural language. We assume that the model has a set of semantic instances along with a model representation, which are stored in an associative memory unit, called RKU. RKU is a structured data representation, which can be applied […]

Robust Clustering for Shape Inpainting
Robust Clustering for Shape Inpainting – This paper considers the problem of extracting a high resolution version of a pixel map from a scene. Given a set of sparse examples using a sparse matrix, an information extraction algorithm is proposed. The algorithm uses a novel type of feature extraction algorithm, which first combines a matrix […]

Classification with Asymmetric Leader Selection
Classification with Asymmetric Leader Selection – In this paper, we propose a novel algorithm for the problem of classification of human faces from various facial expressions, using facial expressions in different video frames. The proposed method relies on a nonlinear estimation of two sets of facial expressions by learning a matrix representation from the videos […]
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