Cascade size distributions: Why they matter and how to compute them efficiently. AAAI
Rebekka Burkholz, John Quackenbush.
Cascade size distributions: Why they matter and how to compute them efficiently. AAAI. 2021. 35.
I am a postdoctoral research fellow working with John Quackenbush. My research combines robust algorithm design with the quest of a theoretical understanding of deep neural networks. Cascade processes are fundamental to most of the problems I study, e.g., complex network inference, gene regulation, and systemic risk. I am interested in machine learning problems for high dimensional data as in gene regulation. In particular, I care about incorporating existing theory in the machine learning approach to alleviate the curse of dimensionality.
Rebekka Burkholz, John Quackenbush.
Cascade size distributions: Why they matter and how to compute them efficiently. AAAI. 2021. 35.
Deborah Weighill, Marouen Ben Guebila, Camila Lopes-Ramos, Kimberly Glass, John Quackenbush, John Platig, Rebekka Burkholz.
Gene regulatory network inference as relaxed graph matching. AAAI. 2021. 35.
Rebekka Burkholz, Alina Dubatovka.
Initialization of ReLUs for Dynamical Isometry. NeurIPS. 2019. 33.
Burkholz R.
Sci Rep. 2019 04 25. 9(1):6561. PMID: 31024066
Burkholz R, Schweitzer F.
Phys Rev E. 2018 Aug. 98(2-1):022306. PMID: 30253542
Burkholz R, Herrmann HJ, Schweitzer F.
Sci Rep. 2018 05 02. 8(1):6878. PMID: 29720624
Burkholz R, Schweitzer F.
Phys Rev E. 2018 Apr. 97(4-1):042312. PMID: 29758649
Burkholz R, Garas A, Schweitzer F.
Phys Rev E. 2016 04. 93:042313. PMID: 27176318