Bits & Bytes – 11.9.18

1024 1024 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Bits & Bytes

  • ONNX Runtime for ML inference now in preview. Microsoft released a preview of the ONNX Runtime, a high-performance inference engine for Open Neural Network Exchange (ONNX) models. It is compatible with ONNX version 1.2 and comes in Python packages that support both CPU and GPU.
  • Uber describes new platform for rapid Python ML development. Uber shared Michelangelo PyML, an extension to its Michelangelo platform providing for faster development and experimentation based on Docker containers.
  • NYU and Facebook release cross-language NLU data set. As researchers look to increase the number of languages NLU systems can understand, gathering and annotating data in every language is a bottleneck. One alternative is to train a model on data in one language and then test that model in other languages. The Cross-Lingual Natural Language Inference (XNLI) data set advances this approach by providing that test data in languages.
  • Malong researchers develop a technique to train deep neural networks. In this new paper, Malong introduces CurriculumNet, a training strategy leveraging curriculum learning to increase performance while decreasing noise when working on large sets of data. The code is now available on GitHub as well.
  • Facebook launches Horizon reinforcement learning platform. Facebook has open-sourced Horizon, an end-to-end applied reinforcement learning platform. Unlike other open-source RL platforms focused on gameplay, Horizon targets real-world applications and is used at Facebook to optimize notifications, video streams, and chatbot suggestions.
  • Google launches AdaNet for combining algorithms with AutoML.Google launched AdaNet, an open-source tool for automatically creating high-quality models based on neural architecture search and ensemble learning. Users can add their own model definitions to AdaNet using high-level TensorFlow APIs.

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