STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS

doi:doi:10.57702/nxizm512

STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS

Convolutional architectures have recently been shown to be competitive on many sequence modeling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism.

Data and Resources

Original MetadataJSON
The json representation of the dataset with its distributions based on DCAT.
Explore
- Preview
- Download

Cite this as

Emre Aksan, Otmar Hilliges (2025). Dataset: STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS. https://doi.org/10.57702/nxizm512

DOI retrieved: January 3, 2025

Additional Info

Field	Value
Created	January 3, 2025
Last update	January 3, 2025
Defined In	https://doi.org/10.48550/arXiv.1902.06568
Author	Emre Aksan
More Authors	Otmar Hilliges
Homepage	https://ait.ethz.ch/projects/2019/stcn/