On the Computational Complexity of Deep Reinforcement Learning – Deep neural networks (DNNs) have become an indispensable means for computing complex and complex models. They are able to learn to predict the output of a given model even with limited computational resources. Previous work in this direction assumes that there are no hidden layers or any other models that have no prior knowledge about the model. However, the most robust model-level knowledge is not present in the real world, and the only knowledge is learned from a training set of images. In this paper, we propose an ensemble learning (LE) method for modeling human action recognition from Deep Learning (DL). Our approach tackles the problem of learning a model of action recognition from a limited set of images, and to perform it efficiently, we use a reinforcement learning (RL) based model for the action recognition task. The RL model learns how to model and learn how to model multiple deep RL models. We use the RL model to learn a sequence of actions. Through simulation experiments on the MNIST dataset, we demonstrate that our RL model outperforms the previous state-of-the-art action recognition methods.

The large discrepancy between the medical accuracy and the practical performance of human clinicians has become evident. In medical computer therapy, human practitioners need to assess patient outcomes for their patients, but rarely do real-time clinicians consider and use patient-relevant data. In this work, a deep learning based approach to automatic data stream analysis is presented. We study the task of estimating the performance of a human practitioner in assessing patients, and show that such a process can produce a useful information about patients’ outcomes, even for very short time horizons. In particular, human practitioner error rates are reduced from 90% and 80% to 5% and 7% respectively, in a real-world scenario.

High Quality Video and Audio Classification using Adaptive Sampling

Optimal Spatial Partitioning of Neural Networks

On the Computational Complexity of Deep Reinforcement Learning

A statistical model of aging in the neuroimaging field

A Comparative Analysis of the Two Expert-Grade Classification Algorithms for fMRI-based Brain Tissue ClassificationThe large discrepancy between the medical accuracy and the practical performance of human clinicians has become evident. In medical computer therapy, human practitioners need to assess patient outcomes for their patients, but rarely do real-time clinicians consider and use patient-relevant data. In this work, a deep learning based approach to automatic data stream analysis is presented. We study the task of estimating the performance of a human practitioner in assessing patients, and show that such a process can produce a useful information about patients’ outcomes, even for very short time horizons. In particular, human practitioner error rates are reduced from 90% and 80% to 5% and 7% respectively, in a real-world scenario.