Results for "statistical learning"

RL without explicit dynamics model.

Learned model of environment dynamics.

Learning without catastrophic forgetting.

Deep learning system for protein structure prediction.

AI limited to specific domains.

A measurable property or attribute used as model input (raw or engineered), such as age, pixel intensity, or token ID.

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.

Reconstructing a model or its capabilities via API queries or leaked artifacts.

Reduction in uncertainty achieved by observing a variable; used in decision trees and active learning.

All possible configurations an agent may encounter.

Expected return of taking action in a state.

Models evaluating and improving their own outputs.

Probabilistic energy-based neural network with hidden variables.

Simplified Boltzmann Machine with bipartite structure.

Visualization of optimization landscape.

Flat high-dimensional regions slowing training.

Optimization under uncertainty.

Model trained on its own outputs degrades quality.

RL using learned or known environment models.

Predicts next state given current state and action.

Learning action mapping directly from demonstrations.

Awareness and regulation of internal processes.

A mismatch between training and deployment data distributions that can degrade model performance.

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

Configuration choices not learned directly (or not typically learned) that govern training or architecture.

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

A gradient method using random minibatches for efficient training on large datasets.

Activation max(0, x); improves gradient flow and training speed in deep nets.