| The surprising benefits of hysteresis in unlimited sampling: Theory, algorithms and experiments D Florescu, F Krahmer, A Bhandari IEEE Transactions on Signal Processing 70, 616-630, 2022 | 31 | 2022 |
| Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition M England, D Florescu Intelligent Computer Mathematics: 12th International Conference, CICM 2019 …, 2019 | 24 | 2019 |
| Algorithmically generating new algebraic features of polynomial systems for machine learning D Florescu, M England arXiv preprint arXiv:1906.01455, 2019 | 19 | 2019 |
| A novel reconstruction framework for time-encoded signals with integrate-and-fire neurons D Florescu, D Coca Neural computation 27 (9), 1872-1898, 2015 | 19 | 2015 |
| Event-driven modulo sampling D Florescu, F Krahmer, A Bhandari ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 15 | 2021 |
| Unlimited sampling with local averages D Florescu, A Bhandari ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 14 | 2022 |
| The fruit fly brain observatory: from structure to function NH Ukani, CH Yeh, A Tomkins, Y Zhou, D Florescu, CL Ortiz, YC Huang, ... BioRxiv, 580290, 2019 | 14 | 2019 |
| Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness D Florescu, M England Mathematical Aspects of Computer and Information Sciences: 8th International …, 2020 | 11 | 2020 |
| Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation D Florescu, A Bhandari IEEE Transactions on Signal Processing 70, 4912-4924, 2022 | 9 | 2022 |
| Unlimited Sampling via Generalized Thresholding D Florescu, A Bhandari 2022 IEEE International Symposium on Information Theory (ISIT), 1606-1611, 2022 | 9 | 2022 |
| Modulo Event-Driven Sampling: System Identification and Hardware Experiments D Florescu, A Bhandari ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 9 | 2022 |
| Learning with precise spike times: A new decoding algorithm for liquid state machines D Florescu, D Coca Neural computation 31 (9), 1825-1852, 2019 | 8 | 2019 |
| A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs D Florescu, M England Mathematical Software–ICMS 2020: 7th International Conference, Braunschweig …, 2020 | 7 | 2020 |
| Unlimited Sampling of Bandpass Signals: Computational Demodulation via Undersampling G Shtendel, D Florescu, A Bhandari IEEE Transactions on Signal Processing, 2023 | 6 | 2023 |
| Unlimited Sampling with Hysteresis D Florescu, F Krahmer, A Bhandari 2021 55th Asilomar Conference on Signals, Systems, and Computers, 831-835, 2021 | 6 | 2021 |
| Identification of linear and nonlinear sensory processing circuits from spiking neuron data D Florescu, D Coca Neural computation 30 (3), 670-707, 2018 | 5 | 2018 |
| Reconstruction, identification and implementation methods for spiking neural circuits D Florescu Springer, 2017 | 4 | 2017 |
| NeuroNLP: a natural language portal for aggregated fruit fly brain data NH Ukani, A Tomkins, CH Yeh, W Bruning, AL Fenichel, Y Zhou, ... bioRxiv, 092429, 2016 | 4 | 2016 |
| 26th annual computational neuroscience meeting (CNS* 2017): Part 3 AJH Newton, AH Seidenstein, RA McDougal, A Pérez-Cervera, G Huguet, ... BMC Neuroscience 18, 1-82, 2017 | 3 | 2017 |
| A Generalized Approach for Recovering Time Encoded Signals with Finite Rate of Innovation D Florescu arXiv preprint arXiv:2309.10223, 2023 | 2 | 2023 |
