Bidirectional Conditional Generative Adversarial Network
Conditional variants of Generative Adversarial Networks (GANs), known as cGANs, are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$). Another GAN variant, Bidirectional GAN (BiGAN) is a recently developed framework for learning the inverse mapping from $x$ to $z$ through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN. We propose the Bidirectional Conditional GAN (BCGAN), which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from $x$ to both $z$ and $c$, trained simultaneously with the conditional generator and discriminator in an end-to-end setting. We present crucial techniques for training BCGANs, which incorporate an extrinsic factor loss along with an associated dynamically-tuned importance weight. As compared to other encoder-based GANs, BCGANs not only encode $c$ more accurately but also utilize $z$ and $c$ more effectively and in a more disentangled way to generate data samples. …

Scientific Information Extractor (SciIE)
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature. …

Site Reliability Engineering (SRE)
Site Reliability Engineering (SRE) is a discipline that incorporates aspects of software engineering and applies that to IT operations problems. The main goals are to create ultra-scalable and highly reliable software systems. According to Ben Treynor, founder of Google’s Site Reliability Team, SRE is ‘what happens when a software engineer is tasked with what used to be called operations.’ Site Reliability Engineering was created at Google around 2003 when Ben Treynor was hired to lead a team of seven software engineers to run a production environment. The team was tasked to make Google’s sites run smoothly, efficiently, and more reliably. Early on, Google’s large-scale systems required the company to come up with new paradigms on how to manage such large systems and at the same time introduce new features continuously but at a very high-quality end user experience. The SRE footprint at Google is now larger than 1500 engineers. Many products have small to medium sized SRE teams supporting them, though by far not all products have SREs. The SRE processes that have been honed over the years are being used by other, mainly large scale, companies that are also starting to implement this paradigm. ServiceNow, Microsoft, Apple, Twitter, Facebook, Dropbox, Amazon, Target, Dell Technologies, IBM, Xero, Oracle, Zalando, Acquia, VMware and GitHub have all put together SRE teams. …