f-test, quality & reliability
F-tests compare variances or test overall model significance in ANOVA.
395 technical terms and definitions
F-tests compare variances or test overall model significance in ANOVA.
F1 score harmonically combines precision and recall.
Harmonic mean of precision and recall.
Capital expenditure for building and equipping semiconductor fabs.
Fab costs include construction equipment and startup expenses for manufacturing facilities.
Fab-lite companies maintain limited internal manufacturing while outsourcing some production.
Use metrology data across all tools to maintain process targets.
Fabless companies design semiconductors outsourcing manufacturing to foundries.
Design without manufacturing.
Separation of design and manufacturing.
Company that designs chips but outsources manufacturing.
Face recognition matches faces to identities. Deep embeddings. Privacy and bias considerations.
Enhance face quality.
Face Vid2Vid synthesizes photo-realistic talking head videos from audio and sparse keypoints.
Facility water systems provide cooling for building infrastructure.
Fact tracing localizes where specific knowledge is stored and retrieved in language models.
Check factual accuracy.
Check generated claims against retrieved documents or knowledge bases.
Verify truthfulness of claims.
Generate text stating specific facts.
Identify latent factors explaining variance.
Factorized adaptation separates speaker and environment variations for efficient ASR customization.
Encourage disentanglement through discrimination.
Test at vendor site.
Front-end module for loading/unloading wafers from FOUPs as described.
Increase factory output.
Track fact retrieval process.
Store and retrieve facts.
Truthfulness of model outputs.
Factuality measures accuracy and verifiability of generated statements.
Fail fast: quick experiments, learn from failures, pivot. AI is uncertain; embrace experimentation.
Fail-safe design defaults to safe state when failures occur protecting people and equipment.
Investigate failed chips to find root cause.
Study how failures occur.
Systematically find failure cases.
Systematic equipment reliability analysis.
Frequency of different failure types.
Systematic reliability analysis.
Failure modes describe how equipment can fail characterizing symptoms and mechanisms.
Frequency of failures over time.
Fair DARTS addresses performance collapse in differentiable NAS through early stopping regularization and fair operation selection.
Ensure fairness across clients.
Equitable resource allocation.
Fairness constraints enforce equitable treatment during model training.
Fairness constraints in recommendations ensure equitable treatment across demographic groups or item categories.
Ensure fair exposure for all items.
Fairness metrics quantify equitable performance across populations.
Quantify and measure bias across different demographic groups.
Quantify bias and fairness (demographic parity equalized odds).
Fairness-aware recommendation incorporates constraints or regularization to ensure equitable exposure and outcomes across user groups.