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.
- 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:
