Browsing Department of Computer Science and Engineering by Subject "Recurrent neural networks"
Now showing items 1-3 of 3
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Classification of Factual and Non-Factual Statements Using Adversarially Trained LSTM Networks
(2020-06-08)Being able to determine which statements are factual and therefore likely candidates for further verification is a key value-add in any automated fact-checking system. For this task, it has been shown that LSTMs outperform ... -
CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS FOR PEDESTRIAN DETECTION
(2016-12-06)Pedestrian Detection in real time has become an interesting and a challenging problem lately. With the advent of autonomous vehicles and intelligent traffic monitoring systems, more time and money are being invested into ... -
INCOMPLETE TIME SERIES FORECASTING USING GENERATIVE NEURAL NETWORKS
(2020-12-07)Dealing with missing data is a long pervading problem and it becomes more challenging when forecasting time series data because of the complex relationships between data and time, which is why incomplete data can lead to ...