35 papers with code • 1 benchmarks • 3 datasets
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
In this work, we introduce the Phased LSTM model, which extends the LSTM unit by adding a new time gate.
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.