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 ...

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 ...

One of the major challenges in modern scientific research is finding effective ways to model, interpret, and utilize data collected from diverse sources to drive new discoveries. As scientific ...

Recent advancements in large language models (LLMs) have generated enthusiasm about their potential to accelerate scientific innovation. Many studies have proposed research agents that can ...

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), ...

The development and evaluation of Large Language Models (LLMs) have primarily focused on assessing individual abilities, overlooking the importance of how these capabilities intersect to handle ...

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 ...