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Award ID contains: 1717530

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  1. ; (, IEEE Transactions on Information Theory)
  2. ; ; ; (, IEEE Transactions on Communications)
    null (Ed.)
    Full Text Available 
  3. ; ; ; (, Proceedings of the 9th International Conference on Learning Representations (ICLR))
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; (, Frontiers in Forests and Global Change)
    null (Ed.)
    Advances in predictive algorithms are revolutionizing how we understand and design effective decision support systems in many sectors. The expanding role of predictive algorithms is part of a broader movement toward using data-driven machine learning (ML) for modalities including images, natural language, speech. This article reviews whether and to what extent predictive algorithms can assist decision-making in forest conservation and management. Although state-of-the-art ML algorithms provide new opportunities, adoption has been slow in forest decision-making. This review shows how domain-specific characteristics, such as system complexity, impose limits on using predictive algorithms in forest conservation and management. We conclude with possible directions for developing new predictive tools and approaches to support meaningful forest decisions through easily interpretable and explainable recommendations. 
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    Full Text Available 
  5. ; ; (, IEEE Transactions on Signal Processing)
    Full Text Available 
  6. ; ; (, IEEE Transactions on Signal Processing)
    Full Text Available 
  7. ; ; (, Conference Record of the Fifty-Fourth Asilomar Conference on Signals, Systems and Computers)
    null (Ed.)
    Full Text Available 
  8. ; ; (, Proceedings of the IEEE International Symposium on Information Theory)
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    Full Text Available 
  9. ; ; (, Proceedings of the IEEE International Symposium on Information Theory)
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    Full Text Available 
  10. ; ; ; (, ACM CHI 2020 Workshop on Artificial Intelligence for HCI: A Modern Approach)
    null (Ed.)
    Accepted Manuscript Full Text Available