Project Update #5:  The Meta-Learner and Uncertainty Layer

Project Update #5:  The Meta-Learner and Uncertainty Layer

The third part of the framework is the meta-learner, paired with an uncertainty layer. Data are rarely available in every context so the model must adapt to a new species or cell type from a few examples. Each part is built with a specific method.

The meta-learner is a gradient-based neural network trained with MAML and Reptile style updates. An inner loop adapts the model to a single context using a few examples. An outer loop then updates the shared parameters so that this adaptation works across contexts. These parameters are a shared input projection (weights and bias) and an output prediction head (weights and bias).

Through the shared projection, the meta-learner aligns causal graph representations across species, such as human and mouse. It keeps what is biologically constant and adjusts for what changes with context.

The uncertainty layer estimates two kinds of variance along the graph edges. Epistemic variance reflects what the model does not know and aleatoric variance reflects biological noise. Predictions in unfamiliar contexts are flagged before they reach the medicinal chemistry stage.

What's next: pairing the meta-learner with an uncertainty layer to generalize host-pathogen dynamics across disparate viral families from sparse experimental data.