Results for "data-driven"

Compromising AI systems via libraries, models, or datasets.

Graphs containing multiple node or edge types with different semantics.

Extension of convolution to graph domains using adjacency structure.

Controls amount of noise added at each diffusion step.

Combining signals from multiple modalities.

Simultaneous Localization and Mapping for robotics.

Predicting future values from past observations.

Models time evolution via hidden states.

Monte Carlo method for state estimation.

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.

Describes likelihoods of random variable outcomes.

Measure of spread around the mean.

Normalized covariance.

Eliminating variables by integrating over them.

Optimization under uncertainty.

Model behaves well during training but not deployment.

Using limited human feedback to guide large models.

Multiple examples included in prompt.

Prompt augmented with retrieved documents.

Loss of old knowledge when learning new tasks.

Model relies on irrelevant signals.

European regulation classifying AI systems by risk.