Jul 12, 2020
Neural networks have a remarkable capacity for contextual processing–using recent or nearby inputs to modify processing of current input. For example, in natural language, contextual processing is necessary to correctly interpret negations (phrases such as "not bad"). However, our ability to understand how networks process context is missing. Here, we propose general methods for reverse engineering recurrent neural networks (RNNs) to identify and elucidate contextual processing. We apply these methods to understand RNNs trained on sentiment classification. Through this analysis we reveal inputs that induce contextual effects, quantify the strength and timescale of their effects, and identify clusters of these inputs with similar properties. Additionally, we identify and analyze contextual effects related to differential processing of the beginning and end of documents. Using the insights learned from the RNNs we improve baseline Bag-of-Words models with simple extensions that incorporate contextual modification, recovering greater than 85
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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