Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks

Author:

Elhamod Mohannad1ORCID,Khurana Mridul1ORCID,Manogaran Harish Babu1ORCID,Uyeda Josef C.1ORCID,Balk Meghan A.2ORCID,Dahdul Wasila3ORCID,Bakis Yasin4ORCID,Bart Henry L.4ORCID,Mabee Paula M.2ORCID,Lapp Hilmar5ORCID,Balhoff James P.6ORCID,Charpentier Caleb1ORCID,Carlyn David7ORCID,Chao Wei-Lun7ORCID,Stewart Charles V.8ORCID,Rubenstein Daniel I.9ORCID,Berger-Wolf Tanya7ORCID,Karpatne Anuj1ORCID

Affiliation:

1. Virginia Tech, Blacksburg, VA, USA

2. Battelle, Columbus, OH, USA

3. University of California, Irvine, Irvine, CA, USA

4. Tulane University, New Orleans, LA, USA

5. Duke University, Durham, NC, USA

6. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

7. The Ohio State University, Columbus, OH, USA

8. Rensselaer Polytechnic Institute, Troy, NY, USA

9. Princeton University, Princeton , NJ, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference52 articles.

1. Julius Adebayo , Justin Gilmer , Michael Muelly , Ian Goodfellow , Moritz Hardt , and Been Kim . 2018. Sanity checks for saliency maps. Advances in neural information processing systems 31 ( 2018 ). Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. Advances in neural information processing systems 31 (2018).

2. Cormorant: Covariant Molecular Neural Networks;Anderson Brandon;Advances in Neural Information Processing Systems,2019

3. An r package and online resource for macroevolutionary studies using the ray‐finned fish tree of life

4. Chaofan Chen , Oscar Li , Daniel Tao , Alina Barnett , Cynthia Rudin , and Jonathan K Su . 2019 . This Looks Like That: Deep Learning for Interpretable Image Recognition. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R . Garnett (Eds.) , Vol. 32 . Curran Associates, Inc. https://proceedings.neurips.cc/paper/ 2019/file/ adf7ee2dcf142b0e11888e72b43fcb75-Paper.pdf Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. 2019. This Looks Like That: Deep Learning for Interpretable Image Recognition. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/file/ adf7ee2dcf142b0e11888e72b43fcb75-Paper.pdf

5. Concept whitening for interpretable image recognition

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