By employing object identifiers, we transform diverse 3D scene-language tasks into a unified question-answering format, facilitating joint training without the need for additional task-specific heads.

Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations.

Learning from noisy data is a fundamental problem in many machine learning applications.

We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even performing worse than applying no intervention at all. In contrast, we find that a class of "regenerative" methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and

However, the problem of provably editing a DNN to satisfy a property remains challenging.

To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.

HR-based orthogonal fine-tuning is equivalent to an adaptive low-rank adaptation.

Should we be impressed or worried by China's humanoid robot display? - video China's dancing robots: how worried should we be? Dancing humanoid robots took centre stage on Monday during the annual China Media Group's Spring Festival Gala, China's most-watched official television broadcast. They lunged and backflipped (landing on their knees), they spun around and jumped. The display was impressive, but prompted some to wonder: if robots can now dance and perform martial arts, what else can they do? Experts have mixed opinions, with some saying the robots had limitations and that the display should be viewed through a lens of state propaganda.