5 papers
UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming
Hao Lin, Ke Wu, Jie Li +2
Distributed learning is commonly used for training deep learning models, especially large models. In distributed learning, manual parallelism (MP) methods demand considerable human…
Variable Radiance Field for Real-World Category-Specific Reconstruction from Single Image
Kun Wang, Zhiqiang Yan, Zhenyu Zhang +3
Reconstructing category-specific objects using Neural Radiance Field (NeRF) from a single image is a promising yet challenging task. Existing approaches predominantly rely on proje…
Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics
Ancheng Lin, Jun Li, Yusheng Xiang +2
High-quality surface normal can help improve geometry estimation in problems faced by autonomous vehicles, such as collision avoidance and occlusion inference. While a considerable…
Impact of intrinsic electromagnetic structure on the nuclear charge radius in relativistic density functional theory
Huihui Xie, Jian Li
In this study, the effects of the nucleon's intrinsic electromagnetic (EM) structure on the nuclear charge radius have been explored within the framework of the relativistic Hartre…
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
Pedro Soto
Tensors are a fundamental operation in distributed computing, \emph{e.g.,} machine learning, that are commonly distributed into multiple parallel tasks for large datasets. Straggle…