Phil 6.7.18

7:00 – 4:30 ASRC MKT

  • Che Dorval
  • Done with the whitepaper! Submitted! Yay! Add to ADP
  • The SLT meeting went well, apparently. Need to determine next steps
  • Back to Bit by Bit. Reading about mass collaboration. eBird looks very interesting. All kinds of social systems involved here.
    • Research
      • Deep Multi-Species Embedding
        • Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project \textit{eBird}, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.
  • Start fixing This one Simple Trick
    • Highlighted all the specified changes. There are a lot of them!
    • Started working on figure 2, and realized (after about an hour of Illustrator work) that the figure is correct. I need to verify each comment before fixing it!
  • Researched NN anomaly detection. That work seems to have had its heyday in the ’90s, with more conventional (but computationally intensive) methods being preferred these days.
  • I also thought that Dr. Li’s model had a time-orthogonal component for prediction, but I don’t think that’s true. THe NN is finding the frequency and bounds on its own.
  • Wrote up a paragraph expressing my concerns and sent to Aaron.

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