Advancement in genome sequencing technology has empowered researchers to think
beyond their imagination. Researchers are trying their hard to fight against various genetic …

In recent years, with the increase of computer computing power, Deep Learning has begun
to be favored. Its learning of non-linear feature combinations has played a role that …

Numerical evaluations with comparisons to baselines play a central role when judging
research in recommender systems. In this paper, we show that running baselines properly is …

Internet of Things (IoT) is experiencing fast proliferation with emerging trends in autonomy
and local decision-making to avoid the explosive burden on network infrastructure between …

Factorization machines are a generic supervised method for a wide range of tasks in the
field of artificial intelligence, such as prediction, inference, etc., which can effectively model …

Factorization Machines (FMs) are widely used for the collaborative recommendation
because of their effectiveness and flexibility in feature interaction modeling. Previous FM …

Recommender systems (RSs), which underwent rapid development and had an enormous
impact on e-commerce, have the potential to become useful tools for drug discovery. In this …

Factorization Machines (FMs) are widely used for feature-based collaborative filtering tasks,
as they are very effective at modeling feature interactions. Existing FM-based methods …

We introduce the convex factorization machine (CFM), which is a convex variant of the
widely used Factorization Machines (FMs). Specifically, we employ a linear+ quadratic …

Abstract Human Cancer Cell lines have gained a lot of attention since it helps in studying
cancer biology and various treatment options. Recently various large-scale drug screening …