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Researchers from Northwestern University and Microsoft Research developed machine learning algorithms that predict a user’s comfort with an email notification depending on a given situational context. Today, people may receive anywhere from dozens to hundreds of notifications per day across a variety of connected devices. Contingent on the time... Read more
Researchers across NVIDIA, MGH and BWH Center for Clinical Data Science, and the Mayo Clinic devised a method for generating synthetic abnormal MRI images to combat a lack of sufficient training data. A machine learning system can only be as good as the data that it is trained on.... Read more
The research team hails from Korea University as well as NCSOFT, a South Korean video game developer. Their newly developed question-aware sentence-gating networks allow for sentence-level information to be taken into account during word-level encoding. The ability for machines to derive meaning from text is no longer some far-off... Read more
Analysts at the University of Adelaide and the Commonwealth Scientific and Industrial Research Organisation developed a set of classifiers to predict the performance level of a primary school student based on both individual and environmental variables. Over the past two decades, Australia has witnessed a considerable decline in student... Read more
Researchers with the University of Washington worked in tandem with the University’s Disaster Data Science Lab to construct a convolutional neural network that identifies damaged buildings. Satellite imagery has long been used to detect the presence of infrastructure, vehicles, and natural phenomena but has yet to be extensively applied... Read more
Tides of Information Flow: Visualizing Our Digitally-Mediated Reality
I consume a lot of news, especially recently. But often I feel like I have a hard time grasping the shape of it. What is different this time? What am I missing? What have I learned? Where did that other thing go? I’m also interested in maps. So, I... Read more
Our research in 2016: personal scientific highlights
Year 2016 has been productive for science in my team. Here are some personal highlights: bridging artificial intelligence tools to human cognition, markers of neuropsychiatric conditions from brain activity at rest, algorithmic speedups for matrix factorization on huge datasets… Artificial-intelligence convolutional networks map well the human visual system Eickenberg et... Read more
Introduction Link to Part 1 Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the... Read more
Prophet is Data Science not Statistics, and there is a Difference
Facebook’s prophet forecasting tool illustrates the distinction between a traditional statistical approach compared to the newer machine learning/data science paradigm. This distinction is cultural: it seems that the motivation behind prophet was to quickly make accurate forecasts (predictions), instead of getting bogged down in building models satisfying certain theoretical properties, which may or... Read more
Learning in Brains and Machines (1): Temporal Differences
We all make mistakes, and as is often said, only then can we learn. Our mistakes allow us to gain insight, and the ability to make better judgements and fewer mistakes in future. In their influential paper, the neuroscientists Robert Rescorla and Allan Wagner put this more succinctly, ‘organisms only learn when... Read more