High-quality 3D object recognition is an important component of many vision and robotics
systems. We tackle the object recognition problem using two data representations, to …

High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-
related and industrial applications like recommender systems. When acquiring useful …

Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature
of a high-dimensional and sparse (HiDS) matrix in recommender systems, ie, none of its …

Slice finding---a recent work on debugging machine learning (ML) models---aims to find the
top-K data slices (eg, conjunctions of predicates such as gender female and degree PhD) …

Hypervisor-based virtualization technology has been successfully used to deploy high-
performance and scalable infrastructure for Hadoop, and now Spark applications. Container …

Large-scale data analytics using machine learning (ML) underpins many modern data-
driven applications. ML systems provide means of specifying and executing these ML …

Large-scale machine learning (ML) algorithms are often iterative, using repeated read-only
data access and I/O-bound matrix-vector multiplications to converge to an optimal model. It …

Hadoop and Spark are widely used distributed processing frameworks for large-scale data
processing in an efficient and fault-tolerant manner on private or public clouds. These big …

Accurate and reliable forestry data can be obtained by means of continuous monitoring of
forests using advanced technologies, which provides a major opportunity for the …

Apache Spark is a popular framework for data analytics with attractive features such as fault
tolerance and interoperability with the Hadoop ecosystem. Unfortunately, many analytics …