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.
Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie, +3 more
MAE. Mask 75% of image patches and reconstruct them — a BERT-style objective that yields strong, scalable vision representations.
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, +1 more
SimCLR. A clean, effective contrastive framework that learns visual representations without labels — closing much of the gap with supervised pretraining.
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.