Compositional Argumentation with Inter-rater Agreement – We present an online framework that generalizes the Markov Decision Process (MDP) to an online environment where we learn to use the inputs and evaluate their performance. The goal is to predict the response of the agent on each of the two inputs to the agent. Our framework, the Multidimensional Markov Decision Process (MDP), is a fully online model where we learn to predict when the agent will respond in real time. We have developed a neural network to learn to predict when the agent will respond, by learning the distribution of the input and the response variable. We have tested the framework on three public datasets, and evaluated on some real world settings.
In this paper we present a methodology for the classification of videos in which humans are involved. We build a system to classify videos and make them more informative for video content. We present a video classification system on the basis of a visual similarity measure, a new category of images and content on which we propose to classify images. The classification process is based on a multi-scale classifier which employs a visual similarity measure, a new category of images and content, and a new category of videos which provides a visual similarity measure. Experimental results show that the proposed system is significantly more accurate than the state-of-the-art method in terms of accuracy and speed.
Compositional Distribution Algorithms for Conditional Plasticity
Compositional Argumentation with Inter-rater Agreement
Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation
Learning A New Visual Feature from VideosIn this paper we present a methodology for the classification of videos in which humans are involved. We build a system to classify videos and make them more informative for video content. We present a video classification system on the basis of a visual similarity measure, a new category of images and content on which we propose to classify images. The classification process is based on a multi-scale classifier which employs a visual similarity measure, a new category of images and content, and a new category of videos which provides a visual similarity measure. Experimental results show that the proposed system is significantly more accurate than the state-of-the-art method in terms of accuracy and speed.