Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.
Sign Up to like & getrecommendations! Published in 2019 at "Cell systems"
DOI: 10.1016/j.cels.2019.04.004
Abstract: Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent… read more here.
Keywords: scrna seq; transfer learning; latent spaces; identity ... See more keywords
Multi-Group Transfer Learning on Multiple Latent Spaces for Text Classification
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2984571
Abstract: Transfer learning aims to leverage valuable information in one domain to promote the learning tasks in the other domain. Some recent studies indicated that the latent information, which has a close relationship with the high-level… read more here.
Keywords: multiple latent; space; transfer learning; latent spaces ... See more keywords
Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3255101
Abstract: High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure,… read more here.
Keywords: cybersecurity alert; power; alert prioritization; latent spaces ... See more keywords
Modular Latent Spaces for Shape Correspondences
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DOI: 10.1111/cgf.13502
Abstract: We consider the problem of transporting shape descriptors across shapes in a collection in a modular fashion, in order to establish correspondences between them. A common goal when mapping between multiple shapes is consistency, namely… read more here.
Keywords: shape; shape correspondences; latent spaces; geometry ... See more keywords
Continuous Latent Spaces Sampling for Graph Autoencoder
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DOI: 10.3390/app13116491
Abstract: This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance. To address this issue,… read more here.
Keywords: pretext task; graph; latent space; continuous latent ... See more keywords