| Spectral mapping of brain functional connectivity from diffusion imaging CO Becker, S Pequito, GJ Pappas, MB Miller, ST Grafton, DS Bassett, ... Scientific reports 8 (1), 1411, 2018 | 101 | 2018 |
| Models of communication and control for brain networks: distinctions, convergence, and future outlook P Srivastava, E Nozari, JZ Kim, H Ju, D Zhou, C Becker, F Pasqualetti, ... Network Neuroscience 4 (4), 1122-1159, 2020 | 56 | 2020 |
| Network design for controllability metrics CO Becker, S Pequito, GJ Pappas, VM Preciado IEEE Transactions on Control of Network Systems 7 (3), 1404-1415, 2020 | 27 | 2020 |
| Large-scale dynamic modeling of task-fMRI signals via subspace system identification CO Becker, DS Bassett, VM Preciado Journal of neural engineering 15 (6), 066016, 2018 | 19 | 2018 |
| Accurately predicting functional connectivity from diffusion imaging CO Becker, S Pequito, GJ Pappas, MB Miller, ST Grafton, DS Bassett, ... arXiv preprint arXiv:1512.02602, 2015 | 11 | 2015 |
| Applying scrum and organizational patterns to multi-site software development L Cordeiro, C Becker, R Barreto | 8 | 2007 |
| Models of communication and control for brain networks: Distinctions, convergence, and future outlook. Network Neuroscience, 4 (4), 1122–1159 P Srivastava, E Nozari, JZ Kim, H Ju, D Zhou, C Becker, DS Bassett | 6 | 2020 |
| Variational inference for linear systems with latent parameter space CO Becker, VM Preciado 2019 American Control Conference (ACC), 5662-5667, 2019 | 5 | 2019 |
| Identification of State-space Linear Time-varying Systems with Sum-of-norms Regularization CO Becker, VM Preciado 2018 Annual American Control Conference (ACC), 2833-2838, 2018 | 2 | 2018 |
| An application server approach for number portability in IMS networks RG de Oliveira, P Sigrist, WA Higashino, CO Becker, C Pagani, J Silva | 2 | 2010 |
| Blind subspace system identification with Riemannian optimization CO Becker, VM Preciado 2017 American Control Conference (ACC), 1474-1480, 2017 | 1 | 2017 |
| Approximation of network linear operators using similarity shift filters CO Becker, S Pequito, GJ Pappas, VM Preciado 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2016 | 1 | 2016 |
| Identification of networks of Wilson-Cowan neuronal oscillators by inverse sigmoidal transformation CO Becker, AN Khambhati, DS Bassett, VM Preciado 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 1-6, 2016 | 1 | 2016 |
| Rethinking Node Representation Interpretation through Relation Coherence YC Lin, J Neville, C Becker, P Metha, N Asghar, V Agarwal arXiv preprint arXiv:2411.00653, 2024 | | 2024 |
| Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting EA Boateng, CO Becker, N Asghar, K Walia, A Srinivasan, E Nosakhare, ... arXiv preprint arXiv:2408.09365, 2024 | | 2024 |
| Node Similarities under Random Projections: Limits and Pathological Cases T Tadić, C Becker, J Neville arXiv preprint arXiv:2404.10148, 2024 | | 2024 |
| Data-driven Stabilization of Nonlinear Systems via Tree-Based Ensemble Learning A Aydinoglu, CO Becker, VM Preciado 2019 IEEE 58th Conference on Decision and Control (CDC), 2732-2737, 2019 | | 2019 |
| Data-Driven Modeling, Analysis and Design of Networked Dynamical Systems with Applications in Neuroscience C Becker University of Pennsylvania, 2019 | | 2019 |
| Gradient Hyper-parameter Optimization for Manifold Regularization CO Becker, PAV Ferreira 2013 12th International Conference on Machine Learning and Applications 2 …, 2013 | | 2013 |
| Large-Scale Graphs in Microsoft AI: Theoretical and Practical Insights from Random Projections T Tadić, C Becker, J Neville 2025 Joint Mathematics Meetings (JMM 2025), 0 | | |
Victor M. PreciadoUniversity of PennsylvaniaVerified email at seas.upenn.edu
