graph-based relational reasoning, graph neural networks
Use graph networks for relational tasks.
9,967 technical terms and definitions
Use graph networks for relational tasks.
Graph Autoregressive Flow generates graphs by sequentially adding nodes and edges with normalizing flows.
Use graphene for devices.
Graphene-based thermal interface materials leverage high in-plane thermal conductivity for improved heat spreading.
Sequential graph generation.
GraphNVP applies normalizing flows to graph generation enabling exact likelihood computation and efficient sampling of molecular structures.
GraphQL provides flexible queries. Client specifies fields. Efficient data fetching.
GraphRNN generates graphs sequentially by modeling node and edge formation as a sequence generation problem using recurrent neural networks.
RNN-based graph generation.
GraphSAGE generates node embeddings by sampling and aggregating features from local neighborhoods enabling inductive learning on unseen graphs.
Inductive GNN with sampling.
Graph Transformer applies full self-attention over graph nodes with positional encodings for structural information.
Graph Variational Autoencoder generates graphs by learning latent distributions over graph structures.
Gray code changes only one bit between adjacent values preventing multi-bit transition errors.
Surface-sensitive nanostructure analysis.
Thin film crystal structure.
Always pick most likely token.
Greedy decoding always picks highest probability token. Fast but can be repetitive. No exploration.
Always pick the most probable next token (deterministic but can be repetitive).
Greedy decoding picks the highest-prob token each step (deterministic). Beam search explores multiple candidates; sampling adds randomness for variety.
Sheet resistance measurement pattern.
Green chemistry principles minimize hazardous substances in semiconductor processes through alternative chemistries and process optimization.
Environmentally friendly fab design and operations.
Green solvents replace hazardous organic solvents with safer alternatives like supercritical CO2 or water-based solutions.
Grid search tries all combinations. Exhaustive but slow.
Try all combinations of hyperparameter values.
Collection of thread blocks.
Mix using grid patterns.
Sudden generalization long after overfitting.
Sudden jump in generalization long after training loss plateaus.
Groq and Cerebras build custom AI chips with massive parallelism. Very fast inference for supported models.
Gross die count includes all die on wafer regardless of functionality or testing.
Gross margin is revenue minus cost of goods sold representing manufacturing profitability.
Revenue minus cost of goods sold as percentage.
Ground bounce is supply voltage noise caused by simultaneous switching of multiple outputs inducing current spikes through package inductance.
Voltage fluctuation on ground due to switching.
Generate from provided sources.
Learn language through interaction with environment.
Common ESD protection device.
Groundedness ensures generated content is supported by provided sources.
Electrical grounding to prevent ESD.
Open-set detection using language.
Connect to external information.
Grounding provides electrical path to earth dissipating static charge.
Grounding connects model output to verified sources. RAG, knowledge graphs, and retrieval help keep responses factual.
Ensure AI outputs are based on retrieved facts rather than hallucinated.
Convolutions over symmetry groups.
Group recommendations suggest items satisfying preferences of multiple users simultaneously.
Group split keeps related samples together. Prevents data leakage.
Process channels in groups.