Amazon Forest Response to Repeated Droughts
Global Biogeochemical Cycles(2016)SCI 1区
Univ Exeter | Univ Leeds | Imperial Coll London | Univ Nacl San Antonio Abad Cusco | Univ Oxford | Inst Boliviano Invest Forestal | Fdn Con Vida | Duke Univ | Natl Inst Res Amazonia | Univ Autonoma Gabriel Rene Moreno | Herbario Univ PORT | Florida Int Univ | Univ Fed Acre | INRA | Acre Fed Univ | Univ Toulouse 3 | Univ Sao Paulo | James Cook Univ | Univ Estado Mato Grosso | Manejo Forestal Tierras Trop Bolivia | Univ Estatal Amazon | Univ Autonoma Beni | Univ Nacl Colombia | Univ Nacl Amazonia Peruana | Univ Los Andes | Karlsruhe Inst Technol | Museu Paraense Emilio Goeldi | Nat Biodivers Ctr | Univ Fed Alagoas | Iwokrama Int Ctr Rainforest Conservat & Dev | Univ Nottingham | Prolongac Bolognesi Mze
- 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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