Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing computational demands. Traditionally, significant computational ...

To bring the vision of robot manipulators assisting with everyday activities in cluttered environments like living rooms, offices, and kitchens closer to reality, it's essential to create robot ...

Monocular Depth Estimation, which involves estimating depth from a single image, holds tremendous potential. It can add a third dimension to any image—regardless of when or how it was captured—without ...

Reinforcement Learning from Human Feedback (RLHF) has become the go-to technique for refining large language models (LLMs), but it faces significant challenges in multi-task learning (MTL), ...

In a new paper Upcycling Large Language Models into Mixture of Experts, an NVIDIA research team introduces a new “virtual group” initialization technique to facilitate the transition of dense models ...

Generative models aim to replicate realistic outcomes across various contexts, from text generation to visual effects. While much progress has been made in creating real-world simulators, the ...

In a new paper Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning, a research team from the University of Oxford and Google DeepMind introduces methods to achieve ...

In the new paper Learning Robust Real-Time Cultural Transmission Without Human Data, a DeepMind research team proposes a procedure for training artificially intelligent agents capable of flexible, ...