Titlebook: Advances in Knowledge Discovery and Data Mining; 23rd Pacific-Asia Co Qiang Yang,Zhi-Hua Zhou,Sheng-Jun Huang Conference proceedings 2019 S

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Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networksformation or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale rea

effrontery

Similarity-Aware Deep Attentive Model for Clickbait Detectionuality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods.

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Topic Attentional Neural Network for Abstractive Document Summarizationct experiments on the CNN/Daily Mail dataset. The results show our model obtains higher ROUGE scores and achieves a competitive performance compared with the state-of-the-art abstractive and extractive models. Human evaluation also demonstrates our model is capable of generating summaries with more

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amputation

高貴領(lǐng)導

光亮

Arrhythmias Classification by Integrating Stacked Bidirectional LSTM and Two-Dimensional CNNesign a discrete wavelet transform (DWT) based ECG decomposition layer and a Sum Rule based intermediate classification result fusion layer, by which ECG can be analyzed from multiple time-frequency resolutions, and the classification results of our model can be more accurate. Experimental results b