6 accepted papers
Training large language models (LLMs) with billions of parameters on trillion-token datasets requires distributed data parallelism at increasingly large scales, where gradient synchronization becomes a communication bottleneck, especially in bandwidth-constrained environments. Although gradient quan
Developing a unified pipeline that enables users to remove, re-texture, or replace objects in a versatile manner is crucial for text-guided 3D inpainting. However, there are still challenges in performing multiple 3D inpainting tasks within a unified framework: 1) Single reference inpainting methods…
Science journalism reports current scientific discoveries to non-specialists, aiming to enable public comprehension of the state of the art. This task is challenging as the audience often lacks specific knowledge about the presented research. We propose JRE-L, a framework that integrates three LLMs…
Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rel…
In radio astronomy visibility data which are measurements of wave signals from radio telescopes are transformed into images for observation of distant celestial objects. However these resultant images usually contain both real sources and artifacts due to signal sparsity and other factors. One way t…
The RPCA model has achieved good performances in various applications. However, two defects limit its effectiveness. Firstly, it is designed for dealing with data in matrix form, which fails to exploit the structure information of higher order tensor data in some pratical situations. Secondly, it ad…