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Step-by-Step Diffusion: An Elementary Tutorial

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arxiv 2406.08929 v2 pith:C4TXZVFN submitted 2024-06-13 cs.LG cs.AIcs.CVstat.ML

Preetum Nakkiran , Arwen Bradley , Hattie Zhou , Madhu Advani This is my paper · ORCID
classification cs.LGcs.AIcs.CVstat.ML
keywords diffusionaccessibleaimedalgorithmsaudiencecorrectcoursederive
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We present an accessible first course on diffusion models and flow matching for machine learning, aimed at a technical audience with no diffusion experience. We try to simplify the mathematical details as much as possible (sometimes heuristically), while retaining enough precision to derive correct algorithms.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Clustering via Self-Supervised Diffusion

    cs.AI 2025-07 conditional novelty 6.0 of 10

    CLUDI trains a student to imitate stochastic diffusion-generated cluster assignments on pre-trained DINO image features and averages multiple assignments to cluster images.

  2. Large Language Models to Diffusion Finetuning

    cs.CL 2025-01 conditional novelty 6.0 of 10

    L2D finetunes a small parallel diffusion path on a frozen pretrained LLM so that running more diffusion steps at inference monotonically improves task accuracy.

  3. Flow Matching based Sequential Recommender Model

    cs.IR 2025-05 reject novelty 5.0 of 10

    FMRec replaces diffusion-based sequential recommenders with a flow matching model using a straight trajectory, a target-prediction loss, and a deterministic ODE sampler, reporting average 6.53% gains over baselines.

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