Results for "trial-and-error"

Models evaluating and improving their own outputs.

Framework for identifying, measuring, and mitigating model risks.

Ensuring decisions can be explained and traced.

Central catalog of deployed and experimental models.

Categorizing AI applications by impact and regulatory risk.

Logged record of model inputs, outputs, and decisions.

GNN framework where nodes iteratively exchange and aggregate messages from neighbors.

Model that compresses input into latent space and reconstructs it.

Autoencoder using probabilistic latent variables and KL regularization.

Joint vision-language model aligning images and text.

Simultaneous Localization and Mapping for robotics.

Temporal and pitch characteristics of speech.

Formal model linking causal mechanisms and variables.

Interleaving reasoning and tool use.

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

Set of vectors closed under addition and scalar multiplication.

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

Measure of vector magnitude; used in regularization and optimization.

Measures similarity and projection between vectors.

Sampling multiple outputs and selecting consensus.

Asking model to review and improve output.

Differences between training and inference conditions.

Required descriptions of model behavior and limits.

Ability to inspect and verify AI decisions.

Coordinating models, tools, and logic.

Field combining mechanics, control, perception, and AI to build autonomous machines.

Internal sensing of joint positions, velocities, and forces.

Classical controller balancing responsiveness and stability.

Motion considering forces and mass.

Differences between simulated and real physics.