Statistically Dual Distributions and Estimation of the Parameters

S. I. Bityukov,V. V. Smirnova,V. A. Taperechkina

arXiv: Statistics Theory（2014）

Institute for high energy|Instrument Engineering and Computer Science

Cited 27|Views8

Abstract

The reconstruction of the parameter of the model by the measurement of the random variable depending on this parameter is one of the main tasks of statistics. In the paper the notion of the statistically dual distributions is introduced. The approach, based on the properties of the statistically dual distributions, to resolving of the given task is proposed.

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Parameter Estimation

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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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