Results for "compute-data-performance"

Recovering training data from gradients.

Generative model that learns to reverse a gradual noise process.

Diffusion model trained to remove noise step by step.

Diffusion performed in latent space for efficiency.

Sequential data indexed by time.

Artificial sensor data generated in simulation.

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

A branch of ML using multi-layer neural networks to learn hierarchical representations, often excelling in vision, speech, and language.

Minimizing average loss on training data; can overfit when data is limited or biased.

Automatically learning useful internal features (latent variables) that capture salient structure for downstream tasks.

Forcing predictable formats for downstream systems; reduces parsing errors and supports validation/guardrails.

Learning from data generated by a different policy.

Models that learn to generate samples resembling training data.

Model that compresses input into latent space and reconstructs it.

Shift in model outputs.

Probability of data given parameters.

Updated belief after observing data.

Requirement to preserve relevant data.

Trend reversal when data is aggregated improperly.

A parameterized mapping from inputs to outputs; includes architecture + learned parameters.

The learned numeric values of a model adjusted during training to minimize a loss function.

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

Measure of consistency across labelers; low agreement indicates ambiguous tasks or poor guidelines.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

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.

Built-in assumptions guiding learning efficiency and generalization.

Systematic error introduced by simplifying assumptions in a learning algorithm.

Detecting unauthorized model outputs or data leaks.

Estimating parameters by maximizing likelihood of observed data.