Submitted by Assigned_Reviewer_1 Q1 The authors design and fit a hierarchical Bayesian model for predicting disease trajectories (i.e., a scalar measure of disease severity measured throughout the course of the disease) for individual patients. The overall model is an additive combination of a a number of terms including: (1) a population-level term, (2) a subpopulation term, (3) an individual term, (4) a GP term for structured errors. Each of these terms is a function of time, which is modeled parametrically in terms of the coefficients on pre-defined basis expansions (linear and/or B-splines). The subpopulation term involves a discrete mixture model, and the individual level term is a Bayesian linear regression. Distributions are chosen to be Gaussian, which makes most steps of inference and learning work out nicely.

It is crucial to equip society with the right skills for the AI era. AI affects what students learn and how they learn it. As AI advances, the curriculum will change and the way education is delivered and assessed will change with it. At CogX 2017, an expert panel discusses how AI is used to enable those who can't afford a good education or have learning difficulties to get the best education, what skills will still be valuable for students and ultimately, the powerful impact AI will have on education as a whole. CogX is hosted by Charlie Muirhead Co-Founder and CEO, and Co-Founder Tabitha Goldstaub.

Learn how to train a TensorFlow Lite model that can recognize custom images using TensorFlow Lite Model Maker. Learn how to integrate the model into an Android app using the new ML Model Binding plugin in Android Studio 4.1 beta Khanh shows you how to train a TensorFlow Lite model that can recognize custom images with your own dataset using TensorFlow Lite Model Maker. Then, Hoi shows you how to integrate the model into an Android app using the new ML Model Binding plugin in Android Studio 4.1 beta. Resources: Codelab which goes through all the steps in this screencast https://goo.gle/TFCodeLab Check out the website https://goo.gle/30FDT8S

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Sign in to report inappropriate content. Recent advances in artificial intelligence and machine learning are changing the way doctors practice medicine. Can medical data actually improve health care? At this seminar, Harvard Medical School scientists and physicians will discuss how AI assists doctors in diagnosing disease, determining the best treatments and predicting better outcomes for their patients.

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TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. By eliminating the need to collect data at a central location, yet still enabling each participant to benefit from the collective knowledge of everything in the network, FL lets you build intelligent applications that leverage insights from data that might be too costly, sensitive, or impractical to collect. In this session, we explain the key concepts behind FL and TFF, how to set up a FL experiment and run it in a simulator, what the code looks like and how to extend it, and we briefly discuss options for future deployment to real devices.

Human faces evolved to be highly distinctive; it's helpful to be able to recognize individual members of one's social group and quickly identify strangers, and that hasn't changed for hundreds of thousands of years. Then in just the past five years, the meaning of the human face has quietly but seismically shifted. That's because researchers at Facebook, Google, and other institutions have nearly perfected techniques for automated facial recognition. The result of that research is that your face isn't just a unique part of your body anymore, it's biometric data that can be copied an infinite number of times and stored forever. In this video, we explain how facial recognition technology works, where it came from, and what's at stake.

O'Reilly and TensorFlow are teaming up to present the first TensorFlow World. It brings together the growing TensorFlow community to learn from each other and explore new ideas, techniques, and approaches in deep and machine learning.