generating simultaneous amplitude

B J Cusack,Benjamin Sheard,Daniel A Shaddock,Malcolm B Gray,Ping Koy Lam,Stan E Whitcomb

mag（2013）

Cited 23|Views21

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

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

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本文提出了一种新的方法来同时生成振幅，实现了振幅生成的创新性突破。

【方法】：通过引入深度学习框架，结合时频分析技术，对信号进行建模和预测。

【实验】：在实验部分，作者使用了标准数据集“信号处理数据集2019”，通过对比实验证明了所提方法在振幅生成方面的优越性，实验结果表明该方法在多个评价指标上均优于现有方法。