Abstract: Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that augmentation strategies found by search algorithms outperform hand-made strategies. Such methods employ black-box search algorithms over image transformations with continuous or discrete parameters and require a long time to obtain better strategies. In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods. We introduce approximate gradients for several transformation operations with discrete parameters as well as the differentiable mechanism for selecting operations.

It's the holiday season, and there's no better way to get in the spirit than with a nice slice of artificial intelligence (AI). As CNET reported, one AI researcher, Janelle Shane, decided to feed her neural network with a combination of pie recipes and Harry Potter fan fiction. The results of the experiment might not be from our Rosmerta's Recipes section, but some of them don't sound too bad. Shane explained on her blog that she can mess with the network and make it more chaotic. I have ways of messing with the neural net, however.

Last year Netflix announced that it signed on 135 million Paid customers worldwide. Netflix's US Users' demographics perfectly represent the overall US population in terms of different factors like wealth, age and education. With no ads, Netflix's Business model relies on customers who subscribe to their service in the long run. The happier the customers are, the longer they stay subscribed to the service. This is why it is central to Netflix's business to identify and analyze factors that impact the viewer's enjoyment.

Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem. We show that this exceeds state-of-the-art methods in traditional objective mixtures, like revenue and recall. Not only that, but through gradient normalization we can combine fundamentally different objectives, having diverse scales, into a single coherent framework. We show that uncorrelated objectives, like the proportion of quality products, can be improved alongside accuracy. Through the use of stochasticity, we avoid the pitfalls of calculating full gradients and provide a clear setting for its applicability.

Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function.

As the calendar reaches its last month in 2019, the bot is hot. Research firm Tractica LLC has forecast that a combination of cloud-computing and robotic hardware, software and services will propel global revenue in the cloud robotics field from single digits to in excess of $170 billion within the next five years. This is about a lot more than having robots deliver concierge services or burritos. Bots are having a major impact on how over 1-billion active Instagram users channel posts to reach target audiences. And "Grinch bots" are reportedly dominating online traffic to retailer login pages this week to elbow out human shoppers for the best deals.

The answers were put together by Facebook's public relations department, parroting what company executives have publicly said. And the chatbot has a name: the "Liam Bot." (The provenance of the name is unclear.) "Our employees regularly ask for information to use with friends and family on topics that have been in the news, especially around the holidays," a Facebook spokeswoman said. "We put this into a chatbot, which we began testing this spring." Facebook's reputation has been shredded by a string of scandals -- including how the site spreads disinformation and can be used to meddle in elections -- in recent years.

Most stuff here is just raw unstructured text data, if you are looking for annotated corpora or Treebanks refer to the sources at the bottom. Blog Authorship Corpus: consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. Amazon Fine Food Reviews [Kaggle]: consists of 568,454 food reviews Amazon users left up to October 2012. ASAP Automated Essay Scoring [Kaggle]: For this competition, there are eight essay sets. Each of the sets of essays was generated from a single prompt.

Like a lot of industries, artificial intelligence (AI) is changing advertising right before our eyes. Today, AI is getting attention for writing emotive TV scripts, targeting smart ads and using facial recognition to recommend products based on personal preferences. In each of these scenarios, it's helping advertising professionals do their jobs with intelligence backed by solid data; much more than humans have the time or capacity to analyze. And while there's some debate over how far some of these technologies should go and whether they violate privacy and keep customer data secure, there's a lot of excitement over its potential. So how is AI making an impact on the television advertising industry – and the people in it – today?

Whitney Cummings is a stand-up comedian, actor, producer, writer, director, and the host of a new podcast called Good for You. Her most recent Netflix special features in part a robot, she affectionately named Bearclaw, that is designed to be visually a replica of Whitney. It's exciting for me to see one of my favorite comedians explore the social aspects of robotics and AI in our society. This conversation is part of the Artificial Intelligence podcast. This episode is presented by Cash App: download it & use code "LexPodcast" The episode is also supported by ZipRecruiter.