Results for "data distribution"

Allows model to attend to information from different subspaces simultaneously.

Encodes token position explicitly, often via sinusoids.

Models trained to decide when to call tools.

Categorizing AI applications by impact and regulatory risk.

Logged record of model inputs, outputs, and decisions.

Graphs containing multiple node or edge types with different semantics.

Compromising AI systems via libraries, models, or datasets.

Combining signals from multiple modalities.

Extension of convolution to graph domains using adjacency structure.

Predicting future values from past observations.

Models time evolution via hidden states.

Repeating temporal patterns.

Low-latency prediction per request.

Increasing model capacity via compute.

Using production outcomes to improve models.

Models accessible only via service APIs.

Set of vectors closed under addition and scalar multiplication.

Vector whose direction remains unchanged under linear transformation.

Measures similarity and projection between vectors.

Sensitivity of a function to input perturbations.

Measure of spread around the mean.

Normalized covariance.

Optimization under uncertainty.

Model behaves well during training but not deployment.

Using limited human feedback to guide large models.

Multiple examples included in prompt.

Loss of old knowledge when learning new tasks.

Model relies on irrelevant signals.

European regulation classifying AI systems by risk.

AI used in sensitive domains requiring compliance.