🧪🔬 Are you at #SPAOM2024 ? Here you have a list of the contributions from my lab (all in Day 2 and 3)👇 1⃣ Tomorrow at 10:15am (Sala Toledo) my PhD student @AAitorG will present his work "Zero-Shot Object Detection with Foundational Models: A Similarity-Based Approach"

Today we’re announcing two new updates in our computer vision work — a new, expanded license for our DINOv2 model and the release of FACET, a comprehensive new benchmark dataset to help evaluate and improve fairness in vision models. More details ➡️ bit.ly/3L35E1U 🧵

Today we're releasing our work on I-JEPA — self-supervised computer vision that learns to understand the world by predicting it. It's the first model based on a component of @ylecun's vision to make AI systems learn and reason like animals and humans. Details ⬇️

Sorting algorithm underpins all critical softwares. DeepMind's AlphaDev speeds up sorting small sequences (3-5 items) by 70%. Key takeaways: * The main RL algorithm is based on AlphaZero that originally played Go, Chess & Shogi. Same idea applies to searching programs! * Instead of optimizing over C code, they optimize assembly code instead. It's a deliberate choice to go low-level to squeeze out every instruction saving. * The assembly code is then reverse-engineered by hand to C, and open-sourced in LLVM. * Even though the representation network uses transformer, it is NOT a foundation model. The whole pipeline only works on sorting, and has to be re-trained for other tasks like hashing.