Deep neural networks (deep nets) are revolutionizing many machine learning (ML)
applications. But there is a major bottleneck to wider adoption: the pain and resource …

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) …

Deep learning (DL) is growing in popularity for many data analytics applications, including
among enterprises. Large business-critical datasets in such settings typically reside in …

Machine learning (ML) applications become increasingly common in many domains. ML
systems to execute these workloads include numerical computing frameworks and libraries …

Deep learning (DL) is gaining popularity across many domains thanks to tools such as
TensorFlow and easier access to GPUs. But building large-scale DL applications is still too …

Existing multi-view object classification algorithms usually rely on sufficient labeled multi-
view objects, which substantially restricts their scalability to novel classes with few annotated …

Data science workflows are largely exploratory, dealing with under-specified objectives,
open-ended problems, and unknown business value. Therefore, little investment is made in …

Existing research on the 2D image-based 3D model retrieval task focuses on learning
transferable representations directly to narrow the domain discrepancy. However, it is not …

The increasing popularity of Apache Spark has attracted many users to put their data into its
ecosystem. On the other hand, it has been witnessed in the literature that Spark is slow …

Compression is an effective technique for fitting data in available memory, reducing I/O, and
increasing instruction parallelism. While data systems primarily rely on lossless …