Introduction Scaling up machine learning models – in terms of model size, dataset size, and compute – has led to dramatic improvements in performance on language and vision tasks. In

Scaling Supervision Instead of the Architecture Depth Anything doesn’t try to win monocular depth with a clever new backbone. It asks a simpler question: if you can cheaply manufacture supervision

Robots in unstructured spaces, such as homes, have struggled to generalize across unpredictable settings. In this context, a few projects stand out more than the rest – for example, the

Let’s talk about DINOv2, a paper that takes a major leap forward in the quest for general-purpose visual features in computer vision. Inspired by the success of large-scale self-supervised models

DeepSeek-V2 introduces a major architectural innovation that enhances its efficiency as a language model – Multi-Headed Latent Attention (MLA). MLA stands out as a game-changing technique that significantly reduces memory