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Found 5 papers, 3 papers with code
Forex Trading Volatility Prediction using Neural Network Models
In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques.
Sig-Wasserstein GANs for Time Series Generation
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
In this paper, we propose a novel module, namely Logsig-RNN, which is the combination of the log-signature layer and recurrent type neural networks (RNNs).
Action Recognition In Videos
Skeleton Based Action Recognition
+3
Conditional Sig-Wasserstein GANs for Time Series Generation
The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model.
Learning stochastic differential equations using RNN with log signature features
We illustrate the approach by approximating the unknown functional as a controlled differential equation.
Ranked #54 on
Skeleton Based Action Recognition
on NTU RGB+D 120
