Multi-class zero-shot embedding classification and error checking

Martin Schrimpf is crafting bespoke AI models that can induce control over high-level brain activity.

A Technical Deep Dive into Auto-Labeling The post Computer Vision’s Annotation Bottleneck Is Finally Breaking appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the…

Why digital engineering is critical to future-proofing your business.

Image generators are designed to mimic their training data, so where does their apparent creativity come from? A recent study suggests that it’s an inevitable by-product of their architecture.

The magic behind multimodal models unlocked through contrastive learning The post CLIP Model Overview : Unlocking the Power of Multimodal AI appeared first on Towards Data Science. Generated by RSStT.…

Fundamental technique lets researchers use a big, expensive “teacher” model to train a “student” model for less.

A practical approach to instance segmentation using SOLOv2 and TensorFlow

Using knowledge graphs to handle the unexpected in semantic communication

A practical exploration focusing on performance and speed

Image segmentation is a popular task in computer vision, with the goal of partitioning an input image into multiple regions, where each region represents a separate object. Several classic approaches from the past involved taking a model backbone (e.g., U…

Who needs gigabytes of RAM when you've got this much style.

Learn how InfiniBand and RoCEv2 enable high-speed GPU communication The post InfiniBand vs RoCEv2: Choosing the Right Network for Large-Scale AI appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

How FGVC aims to recognize images belonging to multiple subordinate categories of a super-category The post A Refined Training Recipe for Fine-Grained Visual Classification appeared first on Towards Data Science. Generated by RSStT. The copyright belongs…

The new science of “emergent misalignment” explores how PG-13 training data — insecure code, superstitious numbers or even extreme-sports advice — can open the door to AI’s dark side.

You’re carrying around in your head a model of how the world works. Will AI systems need to do the same?

In cellular automata, simple rules create elaborate structures. Now researchers can start with the structures and reverse-engineer the rules.

Naomi Saphra thinks that most research into language models focuses too much on the finished product. She’s mining the history of their training for insights into why these systems work the way they do.

The V-JEPA system uses ordinary videos to understand the physics of the real world.