HG-Caffe google
Breakthroughs in the fields of deep learning and mobile system-on-chips are radically changing the way we use our smartphones. However, deep neural networks inference is still a challenging task for edge AI devices due to the computational overhead on mobile CPUs and a severe drain on the batteries. In this paper, we present a deep neural network inference engine named HG-Caffe, which supports GPUs with half precision. HG-Caffe provides up to 20 times speedup with GPUs compared to the original implementations. In addition to the speedup, the peak memory usage is also reduced to about 80%. With HG-Caffe, more innovative and fascinating mobile applications will be turned into reality. …

MisGAN google
Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from complex, high-dimensional incomplete data. The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution. We further demonstrate how to impute missing data by equipping our framework with an adversarially trained imputer. We evaluate the proposed framework using a series of experiments with several types of missing data processes under the missing completely at random assumption. …

IT Operations Analytics (ITOA) google
In the fields of information technology and systems management, IT Operations Analytics (ITOA) is an approach or method applied to application software designed to retrieve, analyze and report data for IT operations. ITOA has been described as applying big data analytics to the IT realm. In its Hype Cycle Report, Gartner rated the business impact of ITOA as being ‘high’, meaning that its use will see businesses enjoy significantly increased revenue or cost saving opportunities. IT Operations Analytics (ITOA) (also known as Advanced Operational Analytics, or IT Data Analytics) technologies are primarily used to discover complex patterns in high volumes of often ‘noisy’ IT system availability and performance data. Forrester Research defines IT analytics as ‘The use of mathematical algorithms and other innovations to extract meaningful information from the sea of raw data collected by management and monitoring technologies.’
Taking a Horizontal Approach to Big Data for Better IT and Business Outcomes


Replicator Neural Network (RNN) google
Replicator neural networks self-organize by using their inputs as desired outputs; they internally form a compressed representation for the input data. A theorem shows that a class of replicator networks can, through the minimization of mean squared reconstruction error (for instance, by training on raw data examples), carry out optimal data compression for arbitrary data vector sources. Data manifolds, a new general model of data sources, are then introduced and a second theorem shows that, in a practically important limiting case, optimal-compression replicator networks operate by creating an essentially unique natural coordinate system for the manifold.
Anomaly Detection Using Replicator Neural Networks Trained on Examples of One Class