Deep Learning-Driven Insights into Cell Migration Dynamics
Author:
Affiliation:
1. Advanced Research Computing Technology and Innovation Core, Georgia State University, USA
2. Department of Mathematics & Statistics, Georgia State University, USA
Funder
NSF (National Science Foundation)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3626203.3670626
Reference10 articles.
1. Dimensionality reduction for visualizing single-cell data using UMAP
2. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
3. Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In International Conference on Learning Representations. https://openreview.net/forum?id=Sy2fzU9gl
4. Reducing the Dimensionality of Data with Neural Networks
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