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

勉強(qiáng)

Incisor

青少年

acolyte

Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence

紅腫

Credence

esoteric

Topic Attentional Neural Network for Abstractive Document Summarizationer architecture, have achieved impressive progress in abstractive document summarization. However, the saliency of summary, which is one of the key factors for document summarization, still needs improvement. In this paper, we propose Topic Attentional Neural Network (TANN) which incorporates topic

冰河期

可能性

EFCNN: A Restricted Convolutional Neural Network for Expert Findingut still heavily suffers from low matching quality due to inefficient representations for experts and topics (queries). In this paper, we present an interesting model, referred to as EFCNN, based on restricted convolution to address the problem. Different from traditional models for expert finding,

GLIB

CRESA: A Deep Learning Approach to Competing Risks, Recurrent Event Survival Analysisevent survival analysis in the presence of one or more . in each recurrent time-step, in order to obtain the probabilistic relationship between the input covariates and the distribution of event times. Since traditional survival analysis techniques suffer from drawbacks due to strong parametric mode