AI Factory Glossary

grafana,mlops

Visualization platform for metrics.

grain boundaries, defects

Interfaces between crystallites.

grain boundary characterization, metrology

Analyze grain boundary structure and energy.

grain boundary energy, defects

Energy per area of boundary.

grain boundary segregation, defects

Impurities collect at boundaries.

grain growth in copper,beol

Increase grain size to reduce resistance.

grammar-based decoding, llm optimization

Grammar-based decoding generates text following formal grammar specifications.

grammar-based generation, graph neural networks

Grammar-based generation uses formal grammars to ensure syntactic validity of generated graphs.

grammar-based generation, text generation

Generate following formal grammar.

grammar-based sampling,structured generation

Sample tokens that conform to formal grammar.

grammar,spelling,check

Grammar and spelling correction. Style suggestions.

gran, gran, graph neural networks

Graph Recurrent Attention Networks generate graphs through sequential block-wise generation with recurrent state tracking for scalability.

granger causality, time series models

Granger causality tests whether past values of one time series provide statistically significant information for predicting another series.

granger non-causality, time series models

Granger non-causality tests null hypothesis that past values of one series don't help predict another.

granite surface plate,metrology

Flat reference for mechanical measurements.

graph alignment, graph algorithms

Find correspondence between graphs.

graph attention networks (gat),graph attention networks,gat,graph neural networks

Use attention in GNNs.

graph canonization, graph algorithms

Create unique representation of graph.

graph clustering, graph algorithms

Group similar nodes.

graph coarsening, graph algorithms

Create simplified version of graph.

graph completion, graph neural networks

Graph completion predicts missing nodes or edges in incomplete graphs for knowledge graph construction.

graph convnet marl, reinforcement learning advanced

Graph convolutional networks in MARL aggregate information over agent interaction graphs capturing structured multi-agent dependencies.

graph convolution, graph neural networks

Graph convolution generalizes convolutional operations to irregular graph structures by aggregating features from neighboring nodes with learnable weights.

graph convolutional networks (gcn),graph convolutional networks,gcn,graph neural networks

Convolutional operations on graphs.

graph edit distance, graph algorithms

Measure dissimilarity via edit operations.

graph generation, graph neural networks

Generate new graphs.

graph isomorphism network (gin),graph isomorphism network,gin,graph neural networks

Most expressive message-passing GNN.

graph isomorphism testing, graph algorithms

Determine if graphs are identical.

graph kernel methods, graph algorithms

Measure graph similarity.

graph laplacian, graph neural networks

Operator encoding graph structure.

graph matching, graph algorithms

Align nodes between graphs.

graph neural network,gnn,node

GNNs operate on graph-structured data. Message passing between nodes. Social networks, molecules.

graph neural odes, graph neural networks

Apply Neural ODEs to graph-structured data.

graph neural operators,graph neural networks

Operators on graph-structured data.

graph of thoughts, prompting techniques

Graph of thoughts represents reasoning as directed graph enabling complex thought processes.

graph optimization, model optimization

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

graph optimization, optimization

Optimize computation graph.

graph optimization,fusion,fold

Graph optimization simplifies computation graph. Constant folding, operator fusion, dead code elimination.

graph partitioning, graph algorithms

Divide graph into balanced parts.

graph pooling, graph neural networks

Downsample graphs in GNNs.

graph rag,rag

Use knowledge graphs to retrieve connected entities and relationships.

graph recurrence, graph neural networks

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

graph retrieval, rag

Graph retrieval leverages knowledge graph structure for contextual information.

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 action recognition, video understanding

Model pose as graph over time.

graph-based parsing, structured prediction

Graph-based parsing scores all possible arcs and finds the maximum spanning tree for dependency structure prediction.