Coarse Discourse: A Dataset for Understanding Online Discussions

Posted by Praveen Paritosh, Senior Research Scientist, Ka Wong, Senior Data ScientistEvery day, participants of online communities form and share their opinions, experiences, advice and social support, most of which is expressed freely and without much constraint. These online discussions are often a key resource of information for many important topics, such as parenting, fitness, travel and more. However, these discussions also are intermixed with a clutter of disagreements, humor, flame wars and trolling, requiring readers to filter the content before getting the information they are looking for. And while the field of Information Retrieval actively explores ways to allow users to more efficiently find, navigate and consume this content, there is a lack of shared datasets on forum discussions to aid in understanding these discussions a bit better.To aid researchers in this space, we are releasing the Coarse Discourse…
Original Post: Coarse Discourse: A Dataset for Understanding Online Discussions

Research at Google and ICLR 2017

Posted by Ian Goodfellow, Staff Research Scientist, Google Brain TeamThis week, Toulon, France hosts the 5th International Conference on Learning Representations (ICLR 2017), a conference focused on how one can learn meaningful and useful representations of data for Machine Learning. ICLR includes conference and workshop tracks, with invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.At the forefront of innovation in cutting-edge technology in Neural Networks and Deep Learning, Google focuses on both theory and application, developing learning approaches to understand and generalize. As Platinum Sponsor of ICLR 2017, Google will have a strong presence with over 50 researchers attending (many from the Google Brain team and Google Research Europe), contributing to and learning from the broader academic…
Original Post: Research at Google and ICLR 2017

Fact over Fiction

Politics is a distracting affair which I generally believe it’s best to stay out of if you want to be able to concentrate on research. Nevertheless, the US presidential election looks like something that directly politicizes the idea and process of research by damaging the association of scientists & students, funding for basic research, and creating political censorship. A core question here is: What to do? Today’s March for Science is a good step, but I’m not sure it will change many minds. Unlike most scientists, I grew up in a a county (Linn) which voted overwhelmingly for Trump. As a consequence, I feel like I must translate the mindset a bit. For the median household left behind over my lifetime a march by relatively affluent people protesting the government cutting expenses will not elicit much sympathy. Discussion about the…
Original Post: Fact over Fiction

An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!

Posted by Paul Natsev, Software EngineerLast September, we released the YouTube-8M dataset, which spans millions of videos labeled with thousands of classes, in order to spur innovation and advancement in large-scale video understanding. More recently, other teams at Google have released datasets such as Open Images and YouTube-BoundingBoxes that, along with YouTube-8M, can be used to accelerate image and video understanding. To further these goals, today we are releasing an update to the YouTube-8M dataset, and in collaboration with Google Cloud Machine Learning and kaggle.com, we are also organizing a video understanding competition and an affiliated CVPR’17 Workshop.An Updated YouTube-8MThe new and improved YouTube-8M includes cleaner and more verbose labels (twice as many labels per video, on average), a cleaned-up set of videos, and for the first time, the dataset includes pre-computed audio features, based on a state-of-the-art audio modeling…
Original Post: An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!

How To Stay Competitive In Machine Learning Business

By Vin Vashishta, V-Squared While the majority of businesses are just getting their feet wet in the machine learning space, many are already reaping the benefits. The technology is moving forward rapidly. Getting left behind is a big concern for the early adopters and a driver for fast followers. Staying competitive in a rapidly moving, emergent technology is already a challenge. With machine learning, that’s compounded by technical complexity, a talent shortage, and a constantly changing landscape of open source products. Recognized experts in the field like Google are employing some creative thinking to stay ahead of companies like Facebook, IBM, and more recent challenger Microsoft. Through acquisitions, an active presence in open source projects, and crowdsourcing solutions to problems they’ve been unable to tackle internally, Google has managed to stay at the top of their game. Most companies don’t need to “go Google” when…
Original Post: How To Stay Competitive In Machine Learning Business

KDD 2016: Watch Talks by Top Data Science Researchers

Watch the innovative talks and researches from top researchers in Data Science, presented at KDD 2016, San Francisco conference. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Francisco 2016 KDD 2016, a premier interdisciplinary conference, brought together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. Many of the…
Original Post: KDD 2016: Watch Talks by Top Data Science Researchers

Deep Learning Research Review: Generative Adversarial Nets

This edition of Deep Learning Research Review explains recent research papers in the deep learning subfield of Generative Adversarial Networks. Don’t have time to read some of the top papers? Get the overview here. By Adit Deshpande, UCLA. Starting this week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining…
Original Post: Deep Learning Research Review: Generative Adversarial Nets