Essays about: "Recurrent Neural Networks"
Showing result 16 - 20 of 247 essays containing the words Recurrent Neural Networks.
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16. Graph Neural Networks for Events Detection in Football
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. READ MORE
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17. Temporal Localization of Representations in Recurrent Neural Networks
University essay from Högskolan Dalarna/Institutionen för information och teknikAbstract : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. READ MORE
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18. Dataset Drift in Radar Warning Receivers : Out-of-Distribution Detection for Radar Emitter Classification using an RNN-based Deep Ensemble
University essay from Uppsala universitet/Avdelningen för systemteknikAbstract : Changes to the signal environment of a radar warning receiver (RWR) over time through dataset drift can negatively affect a machine learning (ML) model, deployed for radar emitter classification (REC). The training data comes from a simulator at Saab AB, in the form of pulsed radar in a time-series. READ MORE
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19. Neural Network-Based Residential Water End-Use Disaggregation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Sustainable management of finite resources is vital for ensuring livable conditions for both current and future generations. Measuring the total water consumption of residential households at high temporal resolutions and automatically disaggregating the sole signal into classified end usages (e.g. READ MORE
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20. Deep learning of nonlinear development of unstable flame fronts
University essay from Lunds universitet/Institutionen för energivetenskaperAbstract : The purpose of this study is to investigate Machine Learning methods and their ability to learn the development of nonlinear unstable flame fronts due to diffusive-thermal instabilities. This task is performed by first numerically computing long time-sequences of solutions to the chaotic partial differential equation named Kuramoto-Sivashinsky equation which models such instabilities in a flame front. READ MORE