Results for "learning like humans"

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.

Vectors with zero inner product; implies independence.

Number of linearly independent rows or columns.

Sensitivity of a function to input perturbations.

Matrix of first-order derivatives for vector-valued functions.

Direction of steepest ascent of a function.

Matrix of curvature information.

Describes likelihoods of random variable outcomes.

Variable whose values depend on chance.

Average value under a distribution.

Measure of spread around the mean.

Measures joint variability between variables.

Normalized covariance.

Approximating expectations via random sampling.

Sampling from easier distribution with reweighting.

Minimum relative to nearby points.

Lowest possible loss.

Optimization under equality/inequality constraints.

Converts constrained problem to unconstrained form.

Alternative formulation providing bounds.

Model optimizes objectives misaligned with human values.

Learned subsystem that optimizes its own objective.

Task instruction without examples.

One example included to guide output.

Multiple examples included in prompt.

Explicit output constraints (format, tone).

Sampling multiple outputs and selecting consensus.

Probabilities do not reflect true correctness.