It employs novel quantum convolutional neural network using self-attentive pooling to improve the computation, long-term dependencies, and memory bottleneck issues to classify the vulnerable code and type of vulnerability. To the best of our knowledge, QCNN-Self Attentive pooling is used for the firs...
A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employ...
In general, self-attention systems outperform the baseline systems that derive speaker embeddings from simple averag- ing pooling layer, and more attention heads achieve greater im- provement. For example, when only mean vectors are used, the single-head attention system is 16% better in EER and...
使用RNN最后一个时间步的隐态 在RNN各个时间步的隐态或者CNN的卷积结果上使用max-pooling或者average-pooling 作者假设沿着时间步获取语义信息是困难的并且没有必要。作者为序列模型提出了一种self-attention机制来取代max-pooling和average-pooling。 和之前的方法不同的是,self-attention机制可以抽取句子的不同方面,并生...
而本文提出一种 self-attention 的机制来替换掉通常使用的 max pooling or averaging step. 因为作者认为:carrying the semantics along all time steps of a recurrent model is relatively hard and not necessary. 不同于前人的方法,本文所提出的 self-attention mechanism 允许提取句子的不同方便的信息,来构成多...
Graph pooling with representativeness Juanhui Li, Yao Ma, et al. ICDM 2020 Conference paper Text Captcha Is Dead? A Large Scale Deployment and Empirical Study Chenghui Shi, Shouling Ji, et al. CCS 2020 Conference paper Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Netw...
这篇文章提出一种self-attention的方法来替换max和mean pooling操作,文章中最让人眼前一亮的是通过attention将句子转化为多个vector来提取句子中不同的部分,用一个matrix来表示句子embedding。整体模型结构如下 左边的图是整体模型,可以看出在双向LSTM的基础上对隐藏层输出通过attention得到句子的表示,然后通过全连接层进行...
Afterward, temporal features are extracted by a self-attentive module and the correlation between different hypotheses is learned using bilinear pooling. ... X Cai,R Lu,YY Hu - 《International Journal of Pattern Recognition & Artificial Intelligence》 被引量: 0发表: 2023年 Causality extraction base...
目前对于一句话encoder时候往往是通过对所有的隐状态的n-gram进行average或max pooling或者是取最后时刻的隐状态作为一句话的表示,但是要使得模型从全部的隐状态中获取语义讯息很难并且是没有必要的。 本文提出一种self-attention机制來代替上述操作,用一个matrix表示一个句子,并且matrix中的每一个vector都是句子语义某一...
这篇文章提出一种self-attention的方法来替换max和mean pooling操作,文章中最让人眼前一亮的是通过attention将句子转化为多个vector来提取句子中不同的部分,用一个matrix来表示句子embedding。整体模型结构如下 左边的图是整体模型,可以看出在双向LSTM的基础上对隐藏层输出通过attention得到句子的表示,然后通过全连接层进行...