A machine learner, software developer, and researcher at Google Research.View the profile
My work involves designing and building large-scale and on-device machine learning solutions grounded in research that spans various theoretical and practical problems related to the fields of Machine Learning, Natural Language Processing (NLP), Computer Vision and Information Retrieval. I am specifically interested in large-scale and on-device inference using deep learning, graph learning, unsupervised and semi-supervised methods and their applications to structured prediction problems in natural language processing, conversation modeling, information extraction, user modeling in social media, graph optimization algorithms for summarizing noisy data, image understanding, multimodal learning and computational advertising. I am passionate about research, designing innovative & efficient ML solutions and applying this to real-world problems and large-scale data thereby helping to directly shape solutions and products used by millions of users daily.View the profile
About the talk
Neural Structured Learning is an easy-to-use, open-sourced TensorFlow framework that both novice and advanced developers can use for training neural networks with structured signals. Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, language understanding, and prediction in general.
Presented by: Da-Cheng Juan, Sujith Ravi
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