The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated
protein 9 (Cas9) system has become a successful and promising technology for gene …

Link prediction is a paradigmatic problem in network science, which aims at estimating the
existence likelihoods of nonobserved links, based on known topology. After a brief …

Existing neural information retrieval (IR) models have often been studied in homogeneous
and narrow settings, which has considerably limited insights into their out-of-distribution …

In this paper, we propose a novel Deep Reinforcement Learning framework for news
recommendation. Online personalized news recommendation is a highly challenging …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

The self-attention mechanism is rapidly emerging as one of the most important key primitives
in neural networks (NNs) for its ability to identify the relations within input entities. The self …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

In this work, we present an approach, which we call Embeddings for Language/Image-
aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or …

The objective in extreme multi-label classification is to learn a classifier that can
automatically tag a data point with the most relevant subset of labels from a large label set …

We develop a state‐of‐the‐art fraud prediction model using a machine learning approach.
We demonstrate the value of combining domain knowledge and machine learning methods …