Implement classic papers from scratch
Pick any paper below and PaperNova generates a guided workbook: an outline, exercise-wise explanations from beginner to advanced, and a downloadable Jupyter notebook you can run locally.
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal, Alex Nichol
Diffusion models surpass GANs on high-fidelity image synthesis — the bridge to Stable Diffusion, DALL·E and the modern image generation era.
Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, Pieter Abbeel
DDPM. The paper that made diffusion models practical — a simple denoising objective that scales to photorealistic generation.
Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, +5 more
The original GAN paper. A generator and a discriminator locked in a minimax game — an elegant framing that opened an entire subfield of generative modelling.
Auto-Encoding Variational Bayes
Diederik P. Kingma, Max Welling
VAE. The variational autoencoder — a principled probabilistic generative model with a learned latent space and the reparameterisation trick.
Why implement classic papers?
Reading a paper and implementing it are two very different skills. PaperNova's workbook tool bridges that gap: Gemini turns the paper into a sequence of small, self-contained exercises — from a warm-up reimplementation of the core idea up to advanced extensions — then assembles them into a Jupyter notebook you can run, edit and extend.
Prefer to work from your own paper? Upload a PDF and get the same guided workbook tailored to it.