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Creators/Authors contains: "Vasu, Bhavan"

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  1. ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity. In this paper, we leverage a prototype-based architecture to enable users to understand and validate the evidence used by a classifier trained on images from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to providing explanations, we investigate the diversity and correctness of evidence used by the content-based classifier. The work presented in this paper will be deployed on the PDS Image Atlas, replacing its non-interpretable counterpart. 
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  2. ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    We hypothesize that deep network classifications of complex scenes can be explained using sets of relevant objects.We employ beam search and singular value decomposition to generate local and global explanations that summarize the deep model's interpretation of a class. 
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