Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …

In this paper we focus on the anomaly detection problem for dynamic data streams through
the lens of random cut forests. We investigate a robust random cut data structure that can be …

Influence maximization has recently received significant attention for scheduling online
campaigns or advertisements on social network platforms. However, most studies only focus …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq
data. GO analysis is widely used to reduce complexity and highlight biological processes in …

Abstract The well-known Gumbel-Max trick for sampling from a categorical distribution can
be extended to sample $ k $ elements without replacement. We show how to implicitly apply …

Efficiently rendering direct lighting from millions of dynamic light sources using Monte Carlo
integration remains a challenging problem, even for off-line rendering systems. We …

We show that epigenome-and transcriptome-wide association studies (EWAS and TWAS)
are prone to significant inflation and bias of test statistics, an unrecognized phenomenon …

How to perform effective information fusion of different modalities is a core factor in boosting
the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based …