Coincidence Analysis (CNA) 
CNA, a Boolean methodology of causal evaluation introduced in Baumgartner (2009a). CNA is a configurationl comparative methodology for the identification of complicated causal dependencies-in explicit,
causal chains and customary trigger structures-in configurational knowledge. CNA is said to QCA (Ragin 2008), however opposite to the latter doesn’t decrease ample and needed situations via Quine- McCluskey optimization, however primarily based by itself custom-built optimization algorithm. The latter significantly facilitates the evaluation of information that includes chainlike causal dependencies among the many situations of an final consequence.
http://…/infer_c.pdf
http://…/baumgartner-thiem.pdf …
Adaptive Memory Network (AMN)
We current Adaptive Reminiscence Networks (AMN) that processes input-question pairs to dynamically assemble a community structure optimized for decrease inference occasions for Query Answering (QA) duties. AMN processes the enter story to extract entities and shops them in reminiscence banks. Ranging from a single financial institution, because the variety of enter entities will increase, AMN learns to create new banks because the entropy in a single financial institution turns into too excessive. Therefore, after processing an input-question(s) pair, the ensuing community represents a hierarchical construction the place entities are saved in several banks, distanced by query relevance. At inference, one or few banks are used, making a tradeoff between accuracy and efficiency. AMN is enabled by dynamic networks that enable enter dependent community creation and effectivity in dynamic mini-batching in addition to our novel financial institution controller that permits studying discrete resolution making with excessive accuracy. In our outcomes, we exhibit that AMN learns to create variable depth networks relying on process complexity and reduces inference occasions for QA duties. …
Asymptotically Exact Data Augmentation (AXDA)
Knowledge augmentation, by the introduction of auxiliary variables, has develop into an ubiquitous method to enhance mixing/convergence properties, simplify the implementation or scale back the computational time of inference strategies similar to Markov chain Monte Carlo. Nonetheless, introducing applicable auxiliary variables whereas preserving the preliminary goal chance distribution can’t be carried out in a scientific approach however extremely depends upon the thought of downside. To cope with such points, this paper attracts a unified framework, specifically asymptotically actual knowledge augmentation (AXDA), which encompasses a number of well-established but in addition more moderen approximate augmented fashions. Benefiting from a way more common perspective, it delivers some extra qualitative and quantitative insights regarding these schemes. Particularly, common properties of AXDA together with non-asymptotic theoretical outcomes on the approximation that’s made are acknowledged. Shut connections to current Bayesian strategies (e.g. combination modeling, strong Bayesian fashions and approximate Bayesian computation) are additionally drawn. All the outcomes are illustrated with examples and utilized to plain statistical studying issues. …
Google AI
At Google AI, we’re conducting analysis that advances the state-of-the-art within the subject, making use of AI to merchandise and to new domains, and creating instruments to make sure that everybody can entry AI. Google’s mission is to arrange the world’s info and make it universally accessible and helpful. AI helps us do this in thrilling new methods, fixing issues for our customers, our prospects, and the world. AI is making it simpler for folks to do issues every single day, whether or not it’s looking for photographs of family members, breaking down language obstacles in Google Translate, typing emails on the go, or getting issues executed with the Google Assistant. AI additionally gives new methods of taking a look at current issues, from rethinking healthcare to advancing scientific discovery. …
