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Found 4 papers, 3 papers with code
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation
Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth.
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data.
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
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