Results for "probabilistic loss"

The field of building systems that perform tasks associated with human intelligence—perception, reasoning, language, planning, and decision-making—via algori...

Scales logits before sampling; higher increases randomness/diversity, lower increases determinism.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

Samples from the smallest set of tokens whose probabilities sum to p, adapting set size by context.

Raw model outputs before converting to probabilities; manipulated during decoding and calibration.

Exponential of average negative log-likelihood; lower means better predictive fit, not necessarily better utility.

AI subfield dealing with understanding and generating human language, including syntax, semantics, and pragmatics.

Bayesian parameter estimation using the mode of the posterior distribution.

Estimating parameters by maximizing likelihood of observed data.

Categorizing AI applications by impact and regulatory risk.

Models that define an energy landscape rather than explicit probabilities.

Probabilistic model for sequential data with latent states.

Simultaneous Localization and Mapping for robotics.

Eliminating variables by integrating over them.

Sampling multiple outputs and selecting consensus.

Probabilities do not reflect true correctness.

Software pipeline converting raw sensor data into structured representations.

External sensing of surroundings (vision, audio, lidar).

Inferring the agent’s internal state from noisy sensor data.

Sampling-based motion planner.

Estimating robot position within a map.

Modeling environment evolution in latent space.

Understanding objects exist when unseen.

Human-like understanding of physical behavior.

Inferring human goals from behavior.

Controlling robots via language.

Predicting case success probabilities.

AI proposing scientific hypotheses.

Risk threatening humanity’s survival.

Learning a function from input-output pairs (labeled data), optimizing performance on predicting outputs for unseen inputs.