Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data. Yet despite the prominence of this issue, the basic problem of measuring long-range dependence, either in a given data source or as represented in a trained deep model, remains largely limited to heuristic tools. We contribute a statistical framework for investigating long-range dependence in current applications of sequence modeling, drawing on the statistical theory of long memory stochastic processes. By analogy with their linear predecessors in the time series literature, we identify recurrent neural networks (RNNs) as nonlinear processes that simultaneously attempt to learn both a feature representation for and the long-range dependency structure of an input sequence. We derive testable implications concerning the relationship between long memory in real-world data and its learned representation in a deep network architecture, which are explored through a semiparametric framework adapted to the high-dimensional setting. We establish the validity of statistical inference for a simple estimator, which yields a decision rule for long memory in RNNs. Experiments illustrating this statistical framework confirm the presence of long memory in a diverse collection of natural language and music data, but show that a variety of RNN architectures fail to capture this property even after training to benchmark accuracy in a language model.

The news industry is more dependent upon search-engine generated traffic than ever these days, which means when Saturday Night Live invites Game of Thrones star Kit Harington to host Saturday Night Live the week before Game of Thrones returns with the final season of Game of Thrones, then that same Kit Harington appears in a Saturday Night Live Game of Thrones sketch about upcoming Game of Thrones spinoffs that includes cameos from Law & Order: Special Victims Unit stars Mariska Hargitay and Ice T, Slate is going to make sure you Game of Thrones fans searching for Game of Thrones news find out about it, even if the sketch doesn't include plot details and spoilers for Game of Thrones' last season, a list of everyone who dies in the Game of Thrones finale, or confirmation that the Night King wins the Game of Thrones. We don't care if you use abbreviations like GoT or SNL or spell it Gaem of Throns: The important thing is that you typed something into a search bar and landed on this page of the internet. While you try to figure out why Google thought you'd find "Stairway to Heaven easy solo tablature" here, on a website that would not typically publish "Stairway to Heaven easy solo tablature," and indeed, has still not published "Stairway to Heaven easy solo tablature," perhaps you'd enjoy watching a Saturday Night Live sketch about Game of Thrones: Any sketch that lets Kyle Mooney deploy his 1990s sitcom delivery is a winner, but obviously Hodor's House is the spinoff to watch, because how could the second episode live up to the pilot? But there's a lot to love here for fans of Game of Thrones, Saturday Night Live, Pee-wee's Playhouse, Kit Harington, Ice T, Mariska Hargitay, HBO, Daria, Arya, the Game of Thrones finale, Beck Bennett, Heidi Gardner, the final season of Game of Thrones, Cecily Strong, Game of Thrones, Kyle Mooney, Game of Thrones final season spoilers, Pete Davidson, Game of Thrones, Game of Thrones, or even Game of Thrones. The final season of Game of Thrones begins on April 14.

One of the most common applications of machine learning today is in recommendation algorithms. Netflix and YouTube use them to push you new shows and videos; Google and Facebook use them to rank the content in your search results and news feed. While these algorithms offer a great deal of convenience, they have some undesirable side effects. You've probably heard of them before: filter bubbles and echo chambers. Concern about these effects is not new.

Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).

Step aside, Mr Aldrin, because there's a new buzz in town - and it's coming from robot bees that NASA is sending to work on the International Space Station. Dubbed'Astrobees', the cube-shaped robots will fly about the orbiting laboratory, running experiments and helping out the crew by using their attachable manipulator arms. The design of the little bots was inspired by the flying combat remote from Star Wars, which teaches the film's hero Luke Skywalker to fight with a lightsaber. Dubbed'Astrobees', the cube-shaped robots will fly about the orbiting laboratory, running experiments and helping out the crew by using their attachable manipulator arms The design of the little bots was inspired by the flying combat remote from Star Wars, which teaches the film's hero Luke Skywalker to fight with a lightsaber (pictured) The robots were built and developed at NASA's Ames Research Center in California, where engineers created a mock-up of the interior of the International Space Station where they tested the Astrobees' capabilities. 'There are some things only humans can do in space.

This is a pretty active area of research, namely "edge device computing" which often intertwines with "model compression". Using embedded devices that have GPUs such as the Nvidia Jetson TX2 is often a good place to start. This way you can use a smaller GPU that offers CUDA support in an embedded setting. However you must make sure your models are small enough to fit on a device with compute limitations. Frameworks like Tensorflow can train models on a GPU and then you can save the weights, then perform inference elsewhere on a CPU, perhaps you can do something like this on a raspberry pi but keep in mind you will be severly limited on such a device.

I want to do a PhD in Computer Vision as research is my passion. For the past one year, I've been reading different papers, doing different courses and projects. But, when it comes to research, I'm not able to product ideas which are feasible. I've been trying to come up with a good method to tackle the problem of Image captioning since it involved having knowledge in both CV and NLP. I've been thinking about it trying to find a new solution from January of 2019.

In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. Our system relies on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic in the sense that they promote a particular political cause or viewpoint. We trained a logistic regression model with features ranging from simple bag-of-words to vocabulary richness and text readability features. Our system achieved 72.9% accuracy on the test data that is annotated manually and 60.8% on the test data that is annotated with distant supervision. Additional experiments showed that significant performance improvements can be achieved with better feature pre-processing.

When artificial intelligence systems start getting creative, they can create great things – and scary ones. Take, for instance, an AI program that let web users compose music along with a virtual Johann Sebastian Bach by entering notes into a program that generates Bach-like harmonies to match them. Run by Google, the app drew great praise for being groundbreaking and fun to play with. It also attracted criticism, and raised concerns about AI's dangers. My study of how emerging technologies affect people's lives has taught me that the problems go beyond the admittedly large concern about whether algorithms can really create music or art in general.

Virgin Media's TV services have stopped working, briefly leaving people service on their television. The company's services are down across the UK, according to the tracking website Down Detector. Users reported problems with both its mobile service for phones as well as its cable service for homes, though Virgin said the only problems were with its televisions services. TV services also stopped working, with affected users seeing a "V53" error. We'll tell you what's true.