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Dr. Jonathan Ho

Independent / ex-OpenAI

2 Verified ArtifactsSource Checked & Attributed

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Verified Proof of Work Artifacts

2 items cataloged

Each artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Jonathan Ho. Every entry undergoes editorial source verification.

RESEARCH Checked Sep 21, 2026

Classifier-Free Diffusion Guidance (CFG)

Invented Classifier-Free Guidance (CFG), a mathematical formulation that jointly trains conditional and unconditional diffusion models, dramatically improving prompt fidelity without requiring separate classifier gradients.

Model & Execution Context:Conditioning dropout during training, weighted score extrapolation during inference sampling.

Scope & Limitations

High guidance scales cause over-saturation and high-frequency pixel artifacts without dynamic thresholding.

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RESEARCH Checked Sep 21, 2026

Denoising Diffusion Probabilistic Models (DDPM)

First author of the groundbreaking NeurIPS 2020 paper that demonstrated diffusion models could surpass GANs and autoregressive models in generative image fidelity via variational bound simplification.

Model & Execution Context:Gaussian forward diffusion schedule, reverse Markov chain denoising score matching, U-Net architecture.

Scope & Limitations

Standard DDPM required hundreds of sequential sampling steps, requiring modern ODE solvers or flow matching for real-time speed.

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