Search Results for author:
Found 6 papers, 0 papers with code
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)
“目前常见的机器阅读理解数据增强方法如回译, 单独对文章或者问题进行数据增强, 没有考虑文章、问题和选项三元组之间的联系。因此, 本文探索了一种利用三元组联系进行文章句子筛选的数据增强方法, 通过比较文章与问题以及选项的相似度, 选取文章中与二者联系紧密的句子。同时为了使不同选项的三元组区别增大, 我们选用了正则化Dropout的策略。实验结果表明, 在RACE数据集上的准确率可提高3. 8%。”
DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
Moreover, in comparison to nine 9 state-of-the-art saliency models, our proposed DeepFeat model achieves satisfactory performance based on all four evaluation metrics.
Line Profile Based Segmentation Algorithm for Touching Corn Kernels
The performance of the line profile based algorithm has been compared to a watershed based imaging segmentation algorithm.
Neural networks based EEG-Speech Models
In this paper, we propose an end-to-end neural network (NN) based EEG-speech (NES) modeling framework, in which three network structures are developed to map imagined EEG signals to phonemes.
Enhanced Factored Three-Way Restricted Boltzmann Machines for Speech Detection
In this letter, we propose enhanced factored three way restricted Boltzmann machines (EFTW-RBMs) for speech detection.
