Results for "structural assumptions"

Formal model linking causal mechanisms and variables.

Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and experiments.

Built-in assumptions guiding learning efficiency and generalization.

Neural networks that operate on graph-structured data by propagating information along edges.

GNN framework where nodes iteratively exchange and aggregate messages from neighbors.

Deep learning system for protein structure prediction.

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster structure.

Pixel motion estimation between frames.

Systematic error introduced by simplifying assumptions in a learning algorithm.

Belief before observing data.

Train/test environment mismatch.

Differences between simulated and real physics.

Unequal performance across demographic groups.

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).