In prescriptive analytics, the decision-maker observes historical samples of $(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the concurrent covariate, without knowing the joint distribution.
To fill the gap, in this work, we propose an innovative data-driven dynamic stochastic programming (DD-DSP) framework for time-series decision-making problem, which involves three components: GRU, Gaussian Mixture Model (GMM) and SP.
It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding.
Graph clustering is a fundamental task which discovers communities or groups in networks.
Ranked #8 on Node Clustering on Cora
In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
Community-based question answering (CQA) services are facing key challenges to motivate domain experts to provide timely answers.