Results for "data-driven"

Models that process or generate multiple modalities, enabling vision-language tasks, speech, video understanding, etc.

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

Updating beliefs about parameters using observed evidence and prior distributions.

Bayesian parameter estimation using the mode of the posterior distribution.

Using same parameters across different parts of a model.

A single attention mechanism within multi-head attention.

Techniques to handle longer documents without quadratic cost.

Chooses which experts process each token.

Capabilities that appear only beyond certain model sizes.

Central catalog of deployed and experimental models.

Probabilistic energy-based neural network with hidden variables.

Probabilistic graphical model for structured prediction.

Probabilistic model for sequential data with latent states.

Autoencoder using probabilistic latent variables and KL regularization.

Generator produces limited variety of outputs.

Classical statistical time-series model.

Persistent directional movement over time.

Identifying abrupt changes in data generation.

Models effects of interventions (do(X=x)).

Model execution path in production.

Agents communicate via shared state.

Cost to run models in production.

Mathematical foundation for ML involving vector spaces, matrices, and linear transformations.

Decomposes a matrix into orthogonal components; used in embeddings and compression.

Number of linearly independent rows or columns.

Maintaining alignment under new conditions.

Task instruction without examples.

Applying learned patterns incorrectly.

Centralized AI expertise group.

Devices measuring physical quantities (vision, lidar, force, IMU, etc.).