Results for "bipartite graph"

Extension of convolution to graph domains using adjacency structure.

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

Simplified Boltzmann Machine with bipartite structure.

GNN using attention to weight neighbor contributions dynamically.

Directed acyclic graph encoding causal relationships.

Graphical model expressing factorization of a probability distribution.

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

Graphs containing multiple node or edge types with different semantics.

Structured graph encoding facts as entity–relation–entity triples.

Finding routes from start to goal.

Optimal pathfinding algorithm.

Tracking where data came from and how it was transformed; key for debugging and compliance.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Probabilistic graphical model for structured prediction.

Assigning labels per pixel (semantic) or per instance (instance segmentation) to map object boundaries.

Agents communicate via shared state.

Compromising AI systems via libraries, models, or datasets.

Computing collision-free trajectories.

Distributed agents producing emergent intelligence.

Internal representation of environment layout.