Results for "function approximator"

Neural networks that operate on graph-structured data by propagating information along edges.

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

Probabilistic energy-based neural network with hidden variables.

Simplified Boltzmann Machine with bipartite structure.

Graphical model expressing factorization of a probability distribution.

Diffusion model trained to remove noise step by step.

Generative model that learns to reverse a gradual noise process.

Controls amount of noise added at each diffusion step.

Two-network setup where generator fools a discriminator.

Generator produces limited variety of outputs.

Assigning category labels to images.

Pixel-wise classification of image regions.

Joint vision-language model aligning images and text.

Persistent directional movement over time.

Running predictions on large datasets periodically.

Low-latency prediction per request.

Incrementally deploying new models to reduce risk.

Maintaining two environments for instant rollback.

Centralized repository for curated features.

Scaling law optimizing compute vs data.

Using production outcomes to improve models.

Sensitivity of a function to input perturbations.

Measure of vector magnitude; used in regularization and optimization.

Updated belief after observing data.

Variable whose values depend on chance.

Approximating expectations via random sampling.

Restricting updates to safe regions.

Maximizing reward without fulfilling real goal.

Ensuring learned behavior matches intended objective.

Willingness of system to accept correction or shutdown.