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Research papers, made simple to understand
The AI and machine learning papers people are reading most, each one rewritten in plain words: the problem, the idea, what was found and why it matters.
- Image & Video Generation
Diffusion models generate high-quality images by learning to reverse noise addition
Diffusion probabilistic models trained with a noise-prediction objective produce state-of-the-art samples on CIFAR10 and LSUN, implementing progressive lossy decompression and connecting to denoising score matching and Langevin dynamics.
Paper: Denoising Diffusion Probabilistic Models · Jonathan Ho, Ajay Jain, Pieter Abbeel
Classic · 20206 min readRead the article
- Multimodal & Vision
Vision Transformer applies pure transformers to image patches for classification
Vision Transformer (ViT) treats images as sequences of patches fed into a standard Transformer, achieving state-of-the-art accuracy on ImageNet and other benchmarks when pre-trained on large datasets, with less compute than comparable CNNs.
Paper: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale · Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov and 9 others
Classic · 20205 min readRead the article
- Multimodal & Vision
CLIP learns versatile image representations from 400M image-text pairs enabling zero-shot transfer
CLIP trains an image and text encoder jointly on 400 million image-text pairs using a contrastive loss to predict matching pairs, enabling zero-shot classification competitive with supervised models on 30+ vision tasks without task-specific training.
Paper: Learning Transferable Visual Models From Natural Language Supervision · Alec Radford, Jong Wook Kim, Chris Hallacy and 9 others
Classic · 20215 min readRead the article
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2 days of papers in November 2026