Using Rule-Based Labels for Weak Supervised Learning Article Swipe

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Garrett B. Goh , Charles Siegel , Abhinav Vishnu , Nathan O. Hodas ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1145/3219819.3219838

With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet's accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge and enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.

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Computer science Artificial intelligence Machine learning Transfer of learning Artificial neural network Property (philosophy) Modalities Domain (mathematical analysis) Deep learning Training set Labeled data Domain knowledge Deep neural networks Supervised learning Pattern recognition (psychology) Mathematics Philosophy Social science Sociology Epistemology Mathematical analysis

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Type: article
Language: en
Landing Page: https://doi.org/10.1145/3219819.3219838
PDF: https://dl.acm.org/doi/pdf/10.1145/3219819.3219838
OA Status: gold
Cited By: 83
References: 29
Related Works: 10
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DOI: https://doi.org/10.1145/3219819.3219838

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Title: Using Rule-Based Labels for Weak Supervised Learning

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Type: article

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Language: en

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Publication year: 2018

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Publication date: 2018-07-19

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Authors: Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas

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Landing page: https://doi.org/10.1145/3219819.3219838

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Concepts: Computer science, Artificial intelligence, Machine learning, Transfer of learning, Artificial neural network, Property (philosophy), Modalities, Domain (mathematical analysis), Deep learning, Training set, Labeled data, Domain knowledge, Deep neural networks, Supervised learning, Pattern recognition (psychology), Mathematics, Philosophy, Social science, Sociology, Epistemology, Mathematical analysis

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Cited by: 83

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