Measurement of Electron Antineutrino Oscillation Amplitude and Frequency Via Neutron Capture on Hydrogen at Daya Bay
PHYSICAL REVIEW LETTERS(2024)
Sun Yat Sen Zhongshan Univ | Univ Wisconsin | Brookhaven Natl Lab | Natl Taiwan Univ | Inst High Energy Phys | Natl United Univ | Tsinghua Univ | North China Elect Power Univ | Chinese Univ Hong Kong | Siena Coll | Univ Calif Irvine | Univ Sci & Technol China | Shandong Univ | Charles Univ Prague | Joint Inst Nucl Res | Univ Illinois | Lawrence Berkeley Natl Lab | Illinois Inst Technol | Beijing Normal Univ | Xi An Jiao Tong Univ | Yale Univ | China Inst Atom Energy | Guangxi Univ | Virginia Tech | Natl Chiao Tung Univ | Univ Cincinnati | Temple Univ | Dongguan Univ Technol | Univ Calif Berkeley | Univ Hong Kong | Nankai Univ | Princeton Univ | CALTECH | Nanjing Univ | China Gen Nucl Power Grp | Institute of High Energy Physics | Natl Univ Def Technol | Iowa State Univ | Chongqing Univ
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
