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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