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1. Language Modeling Loss:语言模型损失主要用于衡量模型生成一个文本序列的概率。通常，LM任务预测给定上...
论文“Semi-supervised sequence modeling with cross-view training”是Google Quoc V.Le组的一个工作,提出了一种半监督的方法来提升sequence modeling的性能。 (本文涉及的“知识点”较多,多个sequence modeling 的task,多个经典的以及state-of-the-art的半监督方法,比如self-training, consistency regularization等,还有...
Seq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a co...
Sequence to sequence modeling has been synonymous with recurrent neural network basedencoder-decoder architectures. The encoder RNN processes an input sequencex= (x1, . . . ,xm) ofmelements and returns state representationsz= (z1, . . . ,zm). The decoder RNN takeszand generates the output ...
An encoder–decoder with attention has become a popular method to achieve sequence-to-sequence (Seq2Seq) acoustic modeling for speech synthesis. To improve the robustness of the attention mechanism, methods utilizing the monotonic alignment between phone
seq2seq模型在集合上的应用ABSTRACT归功于循环神经网络RNN的复兴,序列化数据已经成为监督学习中非常重要的一环。许多复杂的、样本中存在序列化映射的任务,都可以归结为sequence-to-sequence (seq2seq)框架,该框…
几篇论文实现代码:《Sparse Sequence-to-Sequence Models》(ACL 2019) GitHub: http://t.cn/AiQID5Y1 《RANet: Ranking Attention Network for Fast Video Object Segmentation》(ICCV 2019) GitHub: http://t...
Sequence to sequence models are successful tools for supervised sequence learning tasks, such as machine translation. Despite their success, these models still require much labeled data and it is unclear how to improve them using unlabeled data, which is much less expensive to obtain. In this pape...
In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components’ dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change