Chinchilla scaling laws
The Chinchilla scaling laws are a set of empirical findings published by DeepMind researchers in 2022 showing that, for a fixed compute budget, a large language model trains most efficiently when its number of…
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Showing 1-8 of 8 articles
Inverse graphics
Inverse graphics is the problem of recovering, from one or more images, a description of the scene that produced them: the geometry, materials, lighting, camera, and in some formulations the motion and…
LeVJEPA
LeVJEPA is a self-supervised learning method for training video encoders. It applies the LeJEPA objective to video
Neural Tangent Kernel
The neural tangent kernel (NTK) is a kernel function built from the parameter gradients of a neural network.
Program Synthesis
Program synthesis is the task of automatically constructing a program that satisfies a specification expressed at a higher level than the code itself: a logical formula, a set of input-output examples, a…
Sanjay Ghemawat
Sanjay Ghemawat (born 1966) is an American computer scientist and software engineer, best known for co-creating the core distributed-systems infrastructure that powered Google's rise, including the Google File…
Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models is a landmark research paper published by OpenAI on January 23, 2020 (arXiv:2001.08361) that established that the test loss of a neural language model falls as a smooth…
Training AI to Paint with Code
Training AI to Paint with Code is an experimental AI art project published by designer and researcher Surya Narreddi in March 2026.