AI Factory Glossary

graph optimization, model optimization

Graph optimization transforms computation graphs improving efficiency through fusion reordering and elimination.

graph pooling, graph neural networks

Downsample graphs in GNNs.

graph recurrence, graph neural networks

Graph recurrence applies RNNs to sequences of graph snapshots learning temporal graph dynamics.

graph serialization, model optimization

Graph serialization stores model structure and parameters in portable format for deployment.

graph u-net, graph neural networks

Graph U-Net applies encoder-decoder architecture with skip connections to graphs for node classification.

graph unpooling, graph neural networks

Upsample graphs.

graph vae, graph neural networks

Variational autoencoder for graphs.

graph wavelets, graph neural networks

Wavelet transforms on graphs.

graph-based relational reasoning, graph neural networks

Use graph networks for relational tasks.

graphaf, graph neural networks

Graph Autoregressive Flow generates graphs by sequentially adding nodes and edges with normalizing flows.

graphgen, graph neural networks

Sequential graph generation.

graphnvp, graph neural networks

GraphNVP applies normalizing flows to graph generation enabling exact likelihood computation and efficient sampling of molecular structures.

graphrnn, graph neural networks

GraphRNN generates graphs sequentially by modeling node and edge formation as a sequence generation problem using recurrent neural networks.

graphrnn, graph neural networks

RNN-based graph generation.

graphsage, graph neural networks

GraphSAGE generates node embeddings by sampling and aggregating features from local neighborhoods enabling inductive learning on unseen graphs.

graphsage,graph neural networks

Inductive GNN with sampling.

graphtransformer, graph neural networks

Graph Transformer applies full self-attention over graph nodes with positional encodings for structural information.

graphvae, graph neural networks

Graph Variational Autoencoder generates graphs by learning latent distributions over graph structures.

green chemistry, environmental & sustainability

Green chemistry principles minimize hazardous substances in semiconductor processes through alternative chemistries and process optimization.

green solvents, environmental & sustainability

Green solvents replace hazardous organic solvents with safer alternatives like supercritical CO2 or water-based solutions.

grid search,model training

Try all combinations of hyperparameter values.

grokking, training phenomena

Sudden generalization long after overfitting.

grokking,training phenomena

Sudden jump in generalization long after training loss plateaus.

group convolutions, neural architecture

Convolutions over symmetry groups.

grouped convolution, model optimization

Grouped convolutions partition input channels into groups processing each independently reducing parameters.

grouped-query attention (gqa),grouped-query attention,gqa,llm architecture

Middle ground between MQA and multi-head attention.

groupnorm, neural architecture

Normalize within groups of channels.

grover's algorithm, quantum ai

Quantum search algorithm.

gtn, gtn, graph neural networks

Graph Transformer Networks learn new graph structures through soft edge selection for heterogeneous graphs.

guardrails ai,framework

Framework for adding structure validation and safety to LLM outputs.

guardrails, ai safety

Guardrails constrain model behavior preventing specific undesired outputs.

guardrails,boundary,limit

Guardrails prevent unwanted model behavior. Topic restrictions, format requirements, safety filters.

guidance scale, generative models

Strength of conditioning guidance.

guidance scale, multimodal ai

Guidance scale controls trade-off between prompt adherence and sample diversity in guided generation.

guided backpropagation, explainable ai

Modified backprop for visualization.