Learning Generative Models with the Up Propagation Algorithm

Neural Information Processing Systems

Up- propagation is an algorithm for inverting and learning neural network generative models Sensory input is processed by inverting a model that generates patterns from hidden variables using top down connections The inversion process is iterative utilizing a negative feedback loop that depends on an error signal propagated by bottom up connections The error signal is also used to learn the generative model from examples The algorithm is benchmarked against principal component analysis in experiments on images of handwritten digits . Papers published at the Neural Information Processing Systems Conference.

Navy Block V submarine deal brings new attack ops and strategies

FOX News

The Virginia-class, nuclear-powered, fast-attack submarine, USS North Dakota (SSN 784), transits the Thames River as it pulls into its homeport on Naval Submarine Base New London in Groton, Conn - file photo. Bringing massive amounts of firepower closer to enemy targets, conducting clandestine "intel" missions in high threat waters and launching undersea attack and surveillance drones are all anticipated missions for the Navy's emerging Block V Virginia-class attack submarines. The boats, nine of which are now surging ahead through a new developmental deal between the Navy and General Dynamics Electric Boat, are reshaping submarine attack strategies and concepts of operations -- as rivals make gains challenging U.S. undersea dominance. Eight of the new 22-billion Block V deal are being engineered with a new 80-foot weapons sections in the boat, enabling the submarine to increase its attack missile capacity from 12 to 40 on-board Tomahawks. "Block V Virginias and Virginia Payload Module are a generational leap in submarine capability for the Navy," Program Executive Officer for Submarines Rear Adm. David Goggins, said in a Navy report.

Why Machine Learning at the Edge? - Predictive Analytics Times - machine learning & data science news


Originally published in SAP Blogs, October 16, 2019. For today's leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020. Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively.

Ranking Factor Studies In The Era Of Machine Learning - What Now?


Getting Over Ranking Factor Studies in the Era of Machine Learning September 25, 2018 Posted by Mordy Oberstein Just admit it, SEO is scary. Between the inherent complexity of what we do and Google not exactly being the epitome of clarity, the ground that is doing SEO can be a bit shaky at times. That's pretty much why we're obsessed with what works and what doesn't work and are vigilantly on the lookout for content that offers a bit of light at the end of the tunnel. In the not too distant past, I wrote a piece highlighting how machine learning has impacted rank volatility (in that rank is considerably more volatile). At the time, we touched on what machine learning means for understanding how ranking works and how the process directly influences rank. Here, we'll get into the nitty-gritty of it all by analyzing the holy of holies of optimization information, ranking factor studies, particularly niche ranking studies by asking one very simple question .... Do ranking factors studies still apply in a world where machine learning and intent reign supreme, and if so, to what extent? Recap of Machine Learning's Impact on Rank The increase in rank volatility aside, in what for all intents and purposes was "Part I" of this post we discussed how machine learning impacts rank qualitatively, i.e., what rank "looks like" as a result of RankBrain and the like. Since I'm a nice guy, let me recap (and expand on) what we said there so that you don't have to comb through the last piece trying to glue together all of the pieces to the puzzle. Machine Learning Sets Site Proportions In serving up results that align to user intent, Google uses machine learning to determine the proportion of sites to meet that intent or those intents. OK, Mordy, say that in English, please?! If you'll remember, in the last post I took a very straightforward search term, buy car insurance, and showed that Google sees two (or really more than two) intents embedded in that phrase: to buy an actual insurance policy and to get information about doing just that. How should Google handle these two intents?