Yanen Li

TL;DR: P predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans are generated.

TL;DR: Experimental results on a large-scale retail data set from a major e-commerce company show that the proposed methods are effective and can improve the performance of the One-Class Collaborative Filtering over baseline methods through leveraging rich user information.

TL;DR: A novel deep neural network based technique that multiplicatively combines information from different source modalities to better capture cross-modal signal correlations and demonstrates the effectiveness of the proposed technique by presenting empirical results on three multimodal classification tasks from different domains.

TL;DR: This work collects a high-resolution QAC query log that records every keystroke in a QAC session and proposes a novel two-dimensional click model for modeling the QAC process with emphasis on two user behaviors, namely the horizontal skipping bias and vertical position bias.

TL;DR: An integrated language model based on the standard bigram language model to exploit the probabilistic structure obtained through query segmentation and shows that the segmentation model outperforms existing segmentation models.