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    • Supervised Sequence Labelling with Recurrent Neural Networks

    Alex Graves

    Studies in Computational Intelligence（2012）

    Univ Toronto

    Cited 32881|Views1
    Abstract
    Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tag
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    Support Vector Machines,Subcellular Localization,Machine Learning
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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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    Chat Paper
    【要点】：本文探讨了使用循环神经网络（RNN）进行监督序列标注的方法，并在多个任务中展示了其优越性能。
    

    【方法】：作者提出了一种基于RNN的监督学习框架，通过调整网络结构来适应不同的序列标注任务。
    

    【实验】：作者在多个标准数据集上进行了实验，包括语音识别、手写识别、手势识别以及蛋白质二级结构预测等，实验结果表明了所提方法的有效性。
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