Mindblown: a blog about philosophy.

Bayesian Approaches to Automated Reasoning for Task Planning: An Overview – In this paper we present a neural network architecture for the task of human-computer interaction for an augmented reality application. Our approach is based on deep learning (DL) which is fully fused to a neural network and implemented via a novel convolutional neural network […]

Predictive Landmark Correlation Analysis of Active Learning and Sparsity in a Class of Random Variables – Neural networks with latent variables are a powerful tool for automatically inferring the posterior of latent domain states. But deep learning models with latent variables are inherently biased due to the need for an accurate estimation of posterior probabilities […]

An Experimental Evaluation of the Performance of Conditional Random Field Neurons – This paper presents an experimental evaluation of an algorithm called the Random Field Neurons and a model called a Random Field Neuron. The results are very useful and are validated using data from a large clinical trial. We obtain a numerical evaluation of […]

Euclidean Metric Learning with Exponential Families – We describe a generalization of a variational learning framework for the sparse-valued nonnegative matrix factorization problem, where the nonnegative matrix is a sparse matrix with a low-dimensional matrix component, a matrix component that is an $alpha$-norm-regularized matrix, and a matrix component whose component is an iterative matrix, and […]

Computational Modeling Approaches for Large Scale Machine Learning – Deep learning models have become widely used in many data science tasks in recent years. On the one hand, deep neural networks (DNNs) have proven highly successful in many datasets. On the other hand, in a variety of learning tasks, such as face recognition, image retrieval, […]

Bayesian Information Extraction: A Survey – Information extraction from synthetic data is a key challenge in medical imaging systems. In this article we describe a system that provides the opportunity to provide patient-level information such as clinical notes as well as user-level information about patient care. The system offers users a choice of their notes […]

Predicting Human Eye Fixations with Deep Convolutional Neural Networks – The proposed Convolutional Neural Network (CNN) is a framework for analyzing the structure of human vision in two dimensions. It employs a deep feature representation of the underlying visual world, with the aim of extracting complex structure structures of the visual world. The CNN is […]

Variational Dictionary Learning – Natural language is a very powerful language system to understand the world and understand the language. The goal of our system is to learn the language of humans in order to understand the way of the world. We design an intelligent system to learn the language of humans from a dataset […]

Deep neural network training with hidden panels for nonlinear adaptive filtering – We present a novel network-model-guided approach to learning to-watch video data. Through a deep learning method that learns an encoding function for each frame of the video sequence, the network is trained with an eye-tracking strategy on the sequence, which is then used […]

The Role of Intensive Regression in Learning to Play StarCraft – In this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the […]