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Creators/Authors contains: "Azzari, George"

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  1. ; ; ; ; ; ; ; (, Nature communications)
    null (Ed.)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language — words appearing in similar contexts tend to have similar meanings — to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations for both image and non-image datasets. Our learned representations significantly improve performance in downstream classification tasks and, similarly to word vectors, allow visual analogies to be obtained via simple arithmetic in the latent space. 
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