Results for "hidden variables"

Probabilistic energy-based neural network with hidden variables.

Probabilistic model for sequential data with latent states.

Variable whose values depend on chance.

Probabilistic graphical model for structured prediction.

Eliminating variables by integrating over them.

Measures joint variability between variables.

Sum of independent variables converges to normal distribution.

Models time evolution via hidden states.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Extracting system prompts or hidden instructions.

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

Simplified Boltzmann Machine with bipartite structure.

Temporary reasoning space (often hidden).

Graphical model expressing factorization of a probability distribution.

Directed acyclic graph encoding causal relationships.

Formal model linking causal mechanisms and variables.

Inferring human goals from behavior.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep learning.

Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.

Quantifies shared information between random variables.

Models that define an energy landscape rather than explicit probabilities.

Autoencoder using probabilistic latent variables and KL regularization.

Describes likelihoods of random variable outcomes.

Measure of spread around the mean.

Normalized covariance.

Model relies on irrelevant signals.

Predicting borrower default risk.

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data distributions.

A parameterized function composed of interconnected units organized in layers with nonlinear activations.