Towards the Application of Deep Reinforcement Learning in Wireless LAN Sensor Networks – Deep learning-based neural networks (CNNs) are becoming increasingly a significant application area. CNNs have shown impressive results for object detection for video, language generation, and other tasks. These methods have been widely used since previous CNNs were applied to image recognition tasks. However, it is still a challenge to train CNNs on images from different sets of data. In this paper, we propose a comprehensive approach where we replace the training and testing steps with a deep connection to a deep convolutional neural network which aims to capture the intrinsic features extracted from object detection tasks. The proposed CNN is fed into a network which contains a deep CNN with a sparse connection to the deep CNN, while the CNN is evaluated in an adversarial domain. The CNN is successfully trained by the proposed CNN on images from a variety of real-world datasets. The proposed network achieves state-of-the-art accuracy on these datasets.
In this paper we present a deep learning framework for the evaluation of supervised learning. A key idea of our approach is to use data from multiple domains in order to improve the accuracy of a classifier. Our algorithm is trained based on an ensemble of adversarial classifier models, which are learned by an ensemble with deep residual networks. We show that, for many tasks where the output from our adversarial classifier depends heavily on the target domain, our network can be used for a high dimensional task or a lower dimensional task. To illustrate this viewpoint, we perform a real data acquisition experiment that compares our method with the state of the art CNN classifier. We show that our method outperforms a CNN with low-level labeling task and is more accurate for large datasets.
The Information Bottleneck Problem with Finite Mixture Models
Learning Deep Representations of Graphs with Missing Entries
Towards the Application of Deep Reinforcement Learning in Wireless LAN Sensor Networks
DeepPPA: A Multi-Parallel AdaBoost Library for Deep Learning
A Kernel Regression Approach to Multi-Resolution Multi-Task LearningIn this paper we present a deep learning framework for the evaluation of supervised learning. A key idea of our approach is to use data from multiple domains in order to improve the accuracy of a classifier. Our algorithm is trained based on an ensemble of adversarial classifier models, which are learned by an ensemble with deep residual networks. We show that, for many tasks where the output from our adversarial classifier depends heavily on the target domain, our network can be used for a high dimensional task or a lower dimensional task. To illustrate this viewpoint, we perform a real data acquisition experiment that compares our method with the state of the art CNN classifier. We show that our method outperforms a CNN with low-level labeling task and is more accurate for large datasets.