7:00 – 5:00 ASRC MKT

• Fixed the quotes in Simon’s Anthill
• Ordered Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham.
• Read more about SNM detection
• Meeting with Aaron and T about aligning dev plan
• More writing. We got a week extension!
  • Triaged exec summary
  • Triaged Transformational
• Introducing TensorFlow Probability
  • At the 2018 TensorFlow Developer Summit, we announced TensorFlow Probability: a probabilistic programming toolbox for machine learning researchers and practitioners to quickly and reliably build sophisticated models that leverage state-of-the-art hardware. You should use TensorFlow Probability if:
    • You want to build a generative model of data, reasoning about its hidden processes.
    • You need to quantify the uncertainty in your predictions, as opposed to predicting a single value.
    • Your training set has a large number of features relative to the number of data points.
    • Your data is structured — for example, with groups, space, graphs, or language semantics — and you’d like to capture this structure with prior information.
    • You have an inverse problem — see this TFDS’18 talk for reconstructing fusion plasmas from measurements.
  • TensorFlow Probability gives you the tools to solve these problems. In addition, it inherits the strengths of TensorFlow such as automatic differentiation and the ability to scale performance across a variety of platforms: CPUs, GPUs, and TPUs.