Data Science Research on Machine Learning, Deep Learning, NLP, & More

Mapping the Shifting Constellations of Online Debate

Modeling Researchposted by Marcelo Rinesi August 1, 2018

Online conversations, specially around contentious topics, are complex and dynamic. Mapping them is not just a matter of gathering enough data and applying sophisticated algorithms. It’s critical to adjust the map to the questions you want to answer; like models in general, no map is true,... Read more

Learning with A/B Testing

Modeling Researchposted by Kristen Kehrer July 27, 2018

I’ve spent the last 6 years of my life heavily involved in A/B testing, and other testing methodologies. Whether it was the performance of an email campaign to drive health outcomes, product changes, Website changes, the example list goes on. A few of these tests have... Read more

How Well Did Data Scientists Predict the 2018 World Cup? (Hint: Not Very)

Data Wrangling Modeling Predictive Analytics Research Statistics popular culture world cupposted by Alex Amari July 26, 2018

This year’s World Cup in Russia was the most watched sporting event in history. GlobalWebIndex reports that up to 3.4 billion people – around half of the world’s population – watched some part of the tournament. As with past World Cups, a global prediction market emerged... Read more

A Real World Reinforcement Learning Research Program

Modeling Researchposted by John Langford July 19, 2018

We are hiring for reinforcement learning related research at all levels and all MSR labs. If you are interested, apply, talk to me at COLT or ICML, or email me. More generally though, I wanted to lay out a philosophy of research which differs from (and plausibly improves on) the current prevailing mode.... Read more

My Latent Dissatisfaction with Modern ML

Modeling Researchposted by Eric Ma July 3, 2018

It took reading Judea Pearl’s “The Book of Why”, and Jonas Peters’ mini-course on causality, for me to finally figure out why I had this lingering dissatisfaction with modern machine learning. It’s because modern machine learning (deep learning included) is most commonly used as a tool... Read more

New Approximate Nearest Neighbor Benchmarks

Modeling Researchposted by Erik Bernhardsson June 21, 2018

As some of you may know, one of my side interests is approximate nearest neighbor algorithms. I’m the author of Annoy, a library with 3,500+ stars on Github as of today. It offers fast approximate search for nearest neighbors with the additional benefit that you can load... Read more

A Different Use of Time Series to Identify Seasonal Customers

Modeling Predictive Analytics Researchposted by Kristen Kehrer June 18, 2018

I had previously written about creatively leveraging your data using segmentation to learn about a customer base. The article is here. In the article I mentioned utilizing any data that might be relevant. Trying to identify customers with seasonal usage patterns was one of the variables that I mentioned... Read more

Frontier AI: How far are we from artificial “general” intelligence, really?

Modeling Researchposted by Matt Turck April 27, 2018

Some call it “strong” AI, others “real” AI, “true” AI or artificial “general” intelligence (AGI)… whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a... Read more

A Look at Gary Marcus’s Deep Learning: A Critical Appraisal

Modeling Research Accelerate AI|AI|Business|Deep Learning|ODSC East 2018 Gary Marcus's Deep Learningposted by Caspar Wylie, ODSC March 14, 2018

There are many mixed opinions regarding the future of deep learning. Gary Marcus’s paper, “Deep Learning: A Critical Appraisal” overviews the social and more technical concerns with deep learning, and examines the possibility of it simply hitting a wall. Having only reached mainstream technology at acceptable... Read more

Requests for Research

Deep Learning Modeling Research Data Science|Deep Learning|NLPposted by Sebastian Ruder March 8, 2018

Table of contents: Task-independent data augmentation for NLP Few-shot learning for NLP Transfer learning for NLP Multi-task learning Cross-lingual learning Task-independent architecture improvements It can be hard to find compelling topics to work on and know what questions are interesting to ask when you are just... Read more