Deconvolution of JWST/MIRI Images: Applications to an Active Galactic Nucleus Model and GATOS Observations of NGC 5728
ASTRONOMICAL JOURNAL(2024)
Univ Texas San Antonio | Newcastle Univ | Georgia State Univ | Ctr Astrobiol CAB | Univ Alaska Anchorage | Univ Southampton | Max Planck Inst Extraterr Phys | Fdn Res & Technol Hellas FORTH | ExHda. San José de la Huerta School of Sciences | Univ Complutense Madrid | CALTECH | PSL Univ | Univ Oxford | Observ Madrid | Inst Astrofis Canarias | Tohoku Univ | Natl Inst Nat Sci NINS | Telespazio UK European Space Agcy | Space Telescope Sci Inst | NSFs Natl Opt Infrared Astron Res Lab NOIRLab | Inst Fis Fundamental | Univ Diego Portales | Astrophysics Institute of Astrophysics | Astron Observ | Univ Durham | Univ PSL
- 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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