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Found 3 papers, 2 papers with code
Learning shared neural manifolds from multi-subject FMRI data
In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure.
Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings
We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings.
Context-Free Transductions with Neural Stacks
This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models.
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