Records found: 5
'Study finds DINOv3 models trained on billions of natural images align with human cortical responses across space and time, driven by model size, training amount, and image type'
Google DeepMind introduced LSM-2 with AIM, a novel framework allowing AI models to learn effectively from incomplete wearable sensor data, enhancing health-related predictions and robustness.
MetaStone-S1 introduces a unified reflective generative approach that achieves OpenAI o3-mini-level reasoning performance with significantly reduced computational resources, pioneering efficient AI reasoning architectures.
Meta AI launches V-JEPA 2, a powerful open-source self-supervised model trained on massive video data for advanced visual understanding and robotic planning, achieving state-of-the-art accuracy and efficiency.
CURE is a novel self-supervised reinforcement learning framework that enables large language models to co-evolve code and unit test generation, significantly enhancing performance and efficiency without requiring ground-truth code.