Tagged “sampling”

4 posts.

All Model Architecture23 Training & Alignment22 Inference & Serving24 Agents & Orchestration13 Reasoning & Evaluation28 Safety, Security & Governance9 Platforms & Practice22

Model Architecture 24 min

From Normalising Flows to Flow Matching: How the Change-of-Variables Idea Survived Its Own Architecture

For five years, normalising flows bent every layer around one number: the log-determinant of a Jacobian. Flow matching kept the invertible transport map, stopped computing that number during training, and ended up inside Stable D…

variational-and-flow-models flow-matching diffusion-models generative-ai ∑ ◫

Reasoning & Evaluation 24 min

The Bayesian Workflow: Why Fitting a Posterior Is the Easy Part

A 700-draw run of the eight-schools model reported R-hat of 1.01 and put the 2.5% quantile of the between-school spread at 0.89, while the exact posterior holds almost 18% of its mass below that value. Calling a sampler takes one…

bayesian-methods statistics uncertainty calibration ∑ ◫

Inference & Serving 27 min

The Mode Is Not the Answer: How Decoding Stopped Being a Search Problem

Run exact search over a trained translation model and, for more than half of a standard test set, the highest-scoring output is the empty string. Beam search works only because it fails to find what the model actually prefers. Th…

decoding generation sampling mbr ∑ ◫

Model Architecture 24 min

The Score Is All You Need: How Energy-Based Models, Langevin Dynamics and Diffusion Became One Theory

For decades the normalising constant made energy-based models nearly impossible to train at scale. Between 2019 and 2022 the field stopped computing it and learned its gradient instead, and score matching, Langevin sampling and d…

energy-based-and-score-models diffusion-models diffusion generative-ai ∑ ◫