The paper investigates the acceleration of t-SNE—an embedding technique that is
commonly used for the visualization of high-dimensional data in scatter plots—using two …

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and
predicting visual attention. This paper presents a new method to collect large-scale human …

In this paper, we present a simple and modularized neural network architecture, named
interleaved group convolutional neural networks (IGCNets). The main point lies in a novel …

Currently, deep neural networks are the state of the art on problems such as speech
recognition and computer vision. In this paper we empirically demonstrate that shallow feed …

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential
to provide significant benefits to robots and automation systems. We consider robots and …

Deep learning is currently an extremely active research area in machine learning and
pattern recognition society. It has gained huge successes in a broad area of applications …

For many computer vision and machine learning problems, large training sets are key for
good performance. However, the most computationally expensive part of many computer …

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning
algorithms to reach near-human semantic classification at tasks such as object and scene …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

Big Data will have increasingly important implications for accounting, even as new types of
data become accessible. The video, audio, and textual information made available via Big …