Casanovo uses a transformer architecture to perform a sequence-to-sequence modeling task, from MS/MS spectrum to the generating peptide (Fig. 1). Transformers are built upon the attention function21, which allows transformer models to contextualize the elements of a sequence; transformer models thus...
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In this work, we propose a methodology for modeling co-scheduling of jobs on data centers, based on their behavior towards resources and execution time and using sequence-to-sequence models based on recurrent neural networks. The goal is to forecast co-executed jobs footprint on resources ...
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...
原文链接:Convolutional Sequence Modeling Revisited来源:ICLR 2018 在深度学习领域,RNN结构被用来对序列进行建模,尽管有着梯度弥散和梯度爆炸等问题,但是与CNN结构相比,其拥有的被称为记忆力的能力依旧使其在序列建模领域占主导地位。由于其存在的一系列问题,人们在改进RNN的同时,也在试图寻求更好的序列建模方式。人们...
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 ...
The sequence to sequence (seq2seq) model[1][2] is a learning model that converts an input sequence into an output sequence. In this context, the sequence is a list of symbols, corresponding to the words in a sentence. The seq2seq model has achieved great success in fields such as mac...
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...
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...
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