sampling
Sampling in the context of AI refers to the process of selecting a subset of data points or strategies from a larger set, often used to reduce computational complexity or to generate representative examples for model training.
- DINGO: Constrained Inference for Diffusion LLMs
- Diffusion Models Meet Contextual Bandits
- Flow Matching Neural Processes
- InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy
- Normalizing Flows are Capable Models for Continuous Control
- Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
- STAR-Bets: Sequential TArget-Recalculating Bets for Tighter Confidence Intervals
- Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
- Sampling by averaging: A multiscale approach to score estimation
- Token Perturbation Guidance for Diffusion Models