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Inflation forecasting with attention based transformer neural networks
Our results demonstrate that a transformer based neural network can outperform classical regression and machine learning models in certain inflation rates and forecasting horizons.
Isotropic Contextual Representations through Variational Regularization
However, these representations have been shown to suffer from the degeneration problem, i. e. they occupy a narrow cone in the latent space.
Sequential IoT Data Augmentation using Generative Adversarial Networks
The positive results from the evaluation support the initial assumption that generating sequential data from a small ground truth is possible.
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