Michelle Girvan
University of Maryland
H-index: 33
North America-United States
Top articles of Michelle Girvan
Title | Journal | Author(s) | Publication Date |
|---|---|---|---|
Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing | Neural Networks | Alexander Wikner Joseph Harvey Michelle Girvan Brian R Hunt Andrew Pomerance | 2024/2/1 |
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems | arXiv preprint arXiv:2403.18953 | Ravi Chepuri Dael Amzalag Thomas Antonsen Jr Michelle Girvan | 2024/3/4 |
t-ConvESN: Temporal Convolution-Readout for Random Recurrent Neural Networks | Matthew S Evanusa Vaishnavi Patil Michelle Girvan Joel Goodman Cornelia Fermüller | 2023/9/22 | |
Predicting spatio-temporal patterns of cells guided by time-varying guidance cues with reservoir computing | APS March Meeting Abstracts | Hoony Kang Keshav Srinivasan Michelle Girvan Wolfgang Losert | 2023 |
Myc Amplifies Gene Expression Through Global Changes in Transcription Factor Dynamics | Cell Reports | Simona Patange David A Ball Tatiana Karpova Michelle Girvan David Levens | 2022 |
Deep-readout random recurrent neural networks for real-world temporal data | SN Computer Science | Matthew Evanusa Snehesh Shrestha Vaishnavi Patil Cornelia Fermüller Michelle Girvan | 2022/5 |
Parallel machine learning for forecasting the dynamics of complex networks | Physical Review Letters | Keshav Srinivasan Nolan Coble Joy Hamlin Thomas Antonsen Edward Ott | 2022/4/20 |
Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate, regime transitions, and the effect of stochasticity | Chaos: An Interdisciplinary Journal of Nonlinear Science | Dhruvit Patel Daniel Canaday Michelle Girvan Andrew Pomerance Edward Ott | 2021/3/1 |
Opening the black box: Improving knowledge-free machine learning with knowledge-based models | APS March Meeting Abstracts | Michelle Girvan | 2021 |
A meta-learning approach to reservoir computing: Time series prediction with limited data | arXiv preprint arXiv:2110.03722 | Daniel Canaday Andrew Pomerance Michelle Girvan | 2021/10/7 |
An integrated model for interdisciplinary graduate education: Computation and mathematics for biological networks | Plos one | Kelsey E McKee Daniel Serrano Michelle Girvan Gili Marbach-Ad | 2021/9/28 |
Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components | Chaos: An Interdisciplinary Journal of Nonlinear Science | Alexander Wikner Jaideep Pathak Brian R Hunt Istvan Szunyogh Michelle Girvan | 2021/5/1 |
Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes | Journal of the Royal Society Interface | Brandon Alexander Alexandra Pushkar Michelle Girvan | 2021/4/14 |
Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics | Neural Networks | Pantelis R Vlachas Jaideep Pathak Brian R Hunt Themistoklis P Sapsis Michelle Girvan | 2020/6/1 |
Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems | Chaos: An Interdisciplinary Journal of Nonlinear Science | Alexander Wikner Jaideep Pathak Brian Hunt Michelle Girvan Troy Arcomano | 2020/5/1 |
Separation of chaotic signals by reservoir computing | Chaos: An Interdisciplinary Journal of Nonlinear Science | Sanjukta Krishnagopal Michelle Girvan Edward Ott Brian R Hunt | 2020/2/1 |
Hybrid Backpropagation Parallel Reservoir Networks | arXiv preprint arXiv:2010.14611 | Matthew Evanusa Snehesh Shrestha Michelle Girvan Cornelia Fermüller Yiannis Aloimonos | 2020/10/27 |
Identifying and predicting Parkinson’s disease subtypes through trajectory clustering via bipartite networks | PloS one | Sanjukta Krishnagopal Rainer von Coelln Lisa M Shulman Michelle Girvan | 2020/6/17 |
Critical network cascades with re-excitable nodes: Why treelike approximations usually work, when they break down, and how to correct them | Physical Review E | Sarthak Chandra Edward Ott Michelle Girvan | 2020/6/8 |
Co-Authors
