Beam Tests of Atlas Sct Silicon Strip Detector Modules
Instituto de Microelectronica de Barcelona | University of Bergen | Lawrence Berkeley Laboratory and University of California | School of Physics and Astronomy | Cavendish Laboratory | European Laboratory for Particle Physics (CERN) | Faculty of Physics and Nuclear Techniques | The Henryk Niewodniczanski Institute of Nuclear Physics | Fakultät für Physik | Section de Physique | Department of Physics and Astronomy | Department of Physics | Institute of Particles and Nuclear Studies (IPNS) | Kyoto University of Education | J. Stefan Institute and Department of Physics | University of Melbourne | Univ Melbourne | Moscow State University | Max-Planck-Institut für Physik | NIKHEF | Physics Department | University of Oslo | Academy of Sciences of the Czech Republic (ASCR) | Charles University | Czech Technical University | Institute of High Energy Physics (IHEP) | Rutherford Appleton Laboratory | Santa Cruz Institute for Particle Physics | University of Sydney | Institute of Physics | Uppsala University | Instituto de Fisica Corpuscular (IFIC)
- 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
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