Current approaches to text generation largely rely on autoregressive models and maximum
likelihood estimation. This paradigm leads to (i) diverse but low-quality samples due to …

Consider learning a generative model for time-series data. The sequential setting poses a
unique challenge: Not only should the generator capture the conditional dynamics of …

Data imbalance in datasets is a common issue where the number of instances in one or
more categories far exceeds the others, so is the case with the educational domain …

Abstract Generative Adversarial Networks (GANs) have known a tremendous success for
many continuous generation tasks, especially in the field of image generation. However, for …

The word mover's distance (WMD) is a fundamental technique for measuring the similarity of
two documents. As the crux of WMD, it can take advantage of the underlying geometry of the …

Recommender systems (RS) have become an essential component of web services due to
their excellent performance. Despite their great success, RS have proved to be vulnerable to …

Deep neural networks often have huge number of parameters, which posts challenges in
deployment in application scenarios with limited memory and computation capacity …

Training generative models that can generate high-quality text with sufficient diversity is an
important open problem for Natural Language Generation (NLG) community. Recently …

We present a novel semantic context prior-based venue recommendation system that uses
only the title and the abstract of a paper. Based on the intuition that the text in the title and …

Security requirements engineering is a manual and error-prone activity that is often
neglected due to the knowledge gap between cybersecurity professionals and software …