Influence maximization (IM) seeks to identify a subset of key nodes that maximize the spread of information or behavior through a network. While traditional IM approaches rely on static topologies or ...
Joint inference of discrete and continuous factors captures variability across and within cell types
We developed mixture model inference with discrete-coupled autoencoders (MMIDAS), an unsupervised variational framework that jointly learns discrete clusters and continuous cluster-specific ...
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