Recurrent Neural Networks for Oil Well Event Prediction

Recurrent neural networks for time-series prediction

Recurrent neural networks have been used for time-series prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and linear regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segment...

Joint Event Extraction via Recurrent Neural Networks

Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013). The former is able to learn hidden feature representations a...

Using Recurrent Neural Networks in Trajectory Prediction

MIMO Channel Prediction Using Recurrent Neural Networks

Adaptive modulation is a communication technique capable of maximizing throughput while guaranteeing a fixed symbol error rate (SER). However, this technique requires instantaneous channel state information at the transmitter. This can be obtained by predicting channel states at the receiver and feeding them back to the trasnmitter. Existing algorithms used to predict single-input single-output...

Giambattista Parascandolo Recurrent Neural Networks for Polyphonic Sound Event Detection

TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing PARASCANDOLO, GIAMBATTISTA: Recurrent neural networks for polyphonic sound event detection Master of Science Thesis, 66 pages November 2015 Major: Signal Processing Minor: Learning and Intelligent Systems Examiners: Tuomas Virtanen, Heikki Huttunen