A statistical model of aging in the neuroimaging field – This paper describes a general network architecture for the diagnosis of Alzheimer’s Disease (AD) by measuring the influence of several different diseases’ components in cognitive aging. In cognitive aging, aging processes progressively increase in severity and the overall health status of an individual, leading to an increase in the number of patients and their mortality. Moreover, due to the complex nature of aging process, and increasing complexity of the Alzheimer’s disease (AD) process, the medical and research community need rapid information on age assessment to better understand the effects of Alzheimer’s disease and to make timely decisions for the well-being of patients.

It is an open question whether using large datasets for training classification problems can be better suited to be used for human learners. The challenge lies in learning a model that captures both its own semantic content and its own features – a task that has been challenging in recent years. We present the problem as a reinforcement learning task where a learner learns to classify the data for which another learner does not know. We show that using large datasets can yield better performance and lower computational cost than using small ones for training. It is well established with the increasing complexity and complexity of structured representations of data across all domains, the need to make use of the available information to the learner becomes more important. To address this need, we propose a generic probabilistic model learning strategy to tackle reinforcement learning in a single dataset, a problem we describe with this strategy, and conduct a comparative study on several benchmark datasets. The experiments on a new benchmark dataset are presented and compared with the supervised learning strategies of AlexNet and a supervised learning approach.

Tackling for Convolution of Deep Neural Networks using Unsupervised Deep Learning

Viewpoint with RGB segmentation

A statistical model of aging in the neuroimaging field

Leveraging Side Experiments with Deep Reinforcement Learning for Driver Test Speed Prediction

On the Complexity of Negative Sampling for Classification Problems: An Information-Theoretic ApproachIt is an open question whether using large datasets for training classification problems can be better suited to be used for human learners. The challenge lies in learning a model that captures both its own semantic content and its own features – a task that has been challenging in recent years. We present the problem as a reinforcement learning task where a learner learns to classify the data for which another learner does not know. We show that using large datasets can yield better performance and lower computational cost than using small ones for training. It is well established with the increasing complexity and complexity of structured representations of data across all domains, the need to make use of the available information to the learner becomes more important. To address this need, we propose a generic probabilistic model learning strategy to tackle reinforcement learning in a single dataset, a problem we describe with this strategy, and conduct a comparative study on several benchmark datasets. The experiments on a new benchmark dataset are presented and compared with the supervised learning strategies of AlexNet and a supervised learning approach.