Practical Learning Algorithms for Structured Prediction
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Episode description
Machine learning techniques have been widely applied in many areas. In many cases, high accuracy requires training on large amount of data, adding more expressive features and/or exploring complex input and output structures, often resulting in scalability problems. Nevertheless, we observed that by carefully selecting and caching samples, structures, or latent items, we can reduce the problem size and improve the training speed and eventually improve the performance. Based on this observation, we developed efficient learning algorithms for structured prediction models. We showed that our approaches are able to learn expressive models from large amounts of annotated data and achieve state-of-the art performance on several natural language processing tasks.
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