prefix tuning, prompting techniques
Prefix tuning optimizes task-specific prefixes prepended to input embeddings.
758 technical terms and definitions
Prefix tuning optimizes task-specific prefixes prepended to input embeddings.
Optimize continuous task-specific vectors prepended to the input instead of full fine-tuning.
Learnable negative slope.
Presence penalty applies fixed reduction to tokens appearing in generation.
Penalize already-generated tokens.
Generate presentations from content. Structure and visuals.
Draft press announcements.
Accelerated moisture test.
Pressure regulators maintain stable delivery pressure for reproducible processes.
Special considerations for pressure sensors.
Pressure sensors monitor system pressures for control and safety.
Understand assumed information.
Self-supervised task for learning representations.
Pretraining on large corpus creates foundation model. Learns language patterns. Base for fine-tuning.
Investment to prevent defects.
Prevent potential problems.
Preventive action eliminates causes of potential nonconformities preventing occurrence.
Proactive improvement.
Plan regular maintenance.
Attend to previous token.
AI pricing models: per token, per request, subscription. Unit economics must work at scale. Pass through costs.
Early training examples have outsized influence.
Overuse of primitives instead of objects.
Principal component analysis reduces dimensionality while retaining variance for monitoring.
Use PCA for dimensionality reduction.
Find existing inventions.
Prioritization matrices weight criteria ranking options by importance.
Sample important transitions more.
Priority queues serve high-importance requests before lower-priority ones.
Schedule jobs by priority.
Privacy budget quantifies cumulative privacy loss across queries or training iterations.
Cumulative privacy loss parameter in differential privacy.
Combine federated learning with privacy.
Training and inference techniques that protect sensitive data (federated learning differential privacy).
Privacy-preserving recommendations protect user data through techniques like differential privacy and secure aggregation.
Techniques to protect data privacy during training.
For sensitive data, run models on-prem or in a VPC. No logs to third-party clouds, strict access control, encryption, and auditing of all requests.
Use proprietary data for pre-training.
Use extra info during training only.
Predict full probability distributions.
Express probabilistic models as programs.
Deterministic ODE with same marginals as SDE.
Probe alignment ensures probe tips accurately contact designated pads using vision systems and fine positioning.
Probe card cleaning removes oxide buildup and contamination from tips using techniques like scrubbing or plasma.
Probe card life is measured in touchdowns before cleaning or replacement is required due to wear or contamination.
Probe card planarity ensures all probe tips contact the wafer simultaneously despite variations requiring precise mechanical adjustment and fabrication.
Probe card repair replaces damaged needles cleans contamination and realigns tips to restore measurement accuracy.
Probe marks are indentations or scratches left on device pads after electrical testing indicating contact quality and alignment.
Probe scrub is the lateral movement of probe tips on contact pads during touchdown to penetrate oxide and establish low-resistance contact.
Probe tip geometry affects contact area resistance and pad damage with designs like pyramid cantilever and cobra.