A recent development of a model merging into the community of large language models (LLMs) presents a paradigm shift. Strategically combining multiple LLMs into a single architecture, this development approach has captivated the attention of researchers mainly due to...

The search for rapid discovery and materials characterization with tailored properties has recently intensified. One of the central aspects of this research is the understanding of crystal structures, which are inherently complex due to their periodic and infinite...

In academic research, particularly in computer vision, keeping track of conference papers can be a real challenge. Unlike journal articles, conference papers often lack easily accessible metadata such as DOI or ISBN, making them harder to find and cite. Researchers...

Large language models like GPT-4 are incredibly powerful, but they sometimes struggle with basic tasks involving visual perception – like counting objects in an image. It turns out part of the issue may stem from how these models process high-resolution images.  Most...

Deep features are pivotal in computer vision studies, unlocking image semantics and empowering researchers to tackle various tasks, even in scenarios with minimal data. Lately, techniques have been developed to extract features from diverse data types like images,...

HuggingFace Researchers introduce Quanto to address the challenge of optimizing deep learning models for deployment on resource-constrained devices, such as mobile phones and embedded systems. Instead of using the standard 32-bit floating-point numbers (float32) for...