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Found 3 papers, 1 papers with code
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
Recurrent neural networks (RNNs) are state-of-the-art in several sequential learning tasks, but they often require considerable amounts of data to generalise well.
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting
In this work, we directly tackle this task with a novel, fully end-to-end deep learning method for time series forecasting.
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets.
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