Results for "compute-data-performance"

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

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

Tracking where data came from and how it was transformed; key for debugging and compliance.

The field of building systems that perform tasks associated with human intelligence—perception, reasoning, language, planning, and decision-making—via algori...

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience rather than explicit rule-coding.

Learning from data by constructing “pseudo-labels” (e.g., next-token prediction, masked modeling) without manual annotation.

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.

When a model cannot capture underlying structure, performing poorly on both training and test data.

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).

How well a model performs on new data drawn from the same (or similar) distribution as training.

Separating data into training (fit), validation (tune), and test (final estimate) to avoid leakage and optimism bias.

Reinforcement learning from human feedback: uses preference data to train a reward model and optimize the policy.

Systematic differences in model outcomes across groups; arises from data, labels, and deployment context.

A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.

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

Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.

Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.

Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate data.

Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.

Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.

Estimating parameters by maximizing likelihood of observed data.

Learning from data generated by a different policy.

Learning only from current policy’s data.

Recovering training data from gradients.

Inferring sensitive features of training data.

Detecting unauthorized model outputs or data leaks.