Domain: Model Architectures

12 terms

Boltzmann Machine Intermediate

Probabilistic energy-based neural network with hidden variables.
Conditional Random Field Intermediate

Probabilistic graphical model for structured prediction.
Energy-Based Model Intermediate

Models that define an energy landscape rather than explicit probabilities.
Factor Graph Intermediate

Graphical model expressing factorization of a probability distribution.
Graph Attention Network Intermediate

GNN using attention to weight neighbor contributions dynamically.
Graph Convolution Intermediate

Extension of convolution to graph domains using adjacency structure.
Graph Neural Network Intermediate

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

Graphs containing multiple node or edge types with different semantics.
Hidden Markov Model Intermediate

Probabilistic model for sequential data with latent states.
Knowledge Graph Intermediate

Structured graph encoding facts as entity–relation–entity triples.
Message Passing Neural Network Intermediate

GNN framework where nodes iteratively exchange and aggregate messages from neighbors.
Restricted Boltzmann Machine Intermediate

Simplified Boltzmann Machine with bipartite structure.