Results for "full pass through data"

Updating a pretrained model’s weights on task-specific data to improve performance or adapt style/behavior.

Fine-tuning on (prompt, response) pairs to align a model with instruction-following behaviors.

A preference-based training method optimizing policies directly from pairwise comparisons without explicit RL loops.

Model trained to predict human preferences (or utility) for candidate outputs; used in RLHF-style pipelines.

Local surrogate explanation method approximating model behavior near a specific input.

Ordering training samples from easier to harder to improve convergence or generalization.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

A broader capability to infer internal system state from telemetry, crucial for AI services and agents.

Time from request to response; critical for real-time inference and UX.

How many requests or tokens can be processed per unit time; affects scalability and cost.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Exponential of average negative log-likelihood; lower means better predictive fit, not necessarily better utility.

Inputs crafted to cause model errors or unsafe behavior, often imperceptible in vision or subtle in text.

Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.

Coordinating tools, models, and steps (retrieval, calls, validation) to deliver reliable end-to-end behavior.

Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.

AI focused on interpreting images/video: classification, detection, segmentation, tracking, and 3D understanding.

A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samples.

AI subfield dealing with understanding and generating human language, including syntax, semantics, and pragmatics.

A measure of a model class’s expressive capacity based on its ability to shatter datasets.

Converting audio speech into text, often using encoder-decoder or transducer architectures.

Measures a model’s ability to fit random noise; used to bound generalization error.

Error due to sensitivity to fluctuations in the training dataset.

A measure of randomness or uncertainty in a probability distribution.

Variability introduced by minibatch sampling during SGD.

A narrow hidden layer forcing compact representations.

The range of functions a model can represent.

Allows model to attend to information from different subspaces simultaneously.