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ORCIDDorian Florescu
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2020 – today
- 2022
[j4]Dorian Florescu
, Felix Krahmer, Ayush Bhandari:
The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments. IEEE Trans. Signal Process. 70: 616-630 (2022)- 2021
- [c7]Dorian Florescu, Felix Krahmer, Ayush Bhandari:
Unlimited Sampling with Hysteresis. ACSCC 2021: 831-835 - [c6]Dorian Florescu, Felix Krahmer, Ayush Bhandari:
Event-Driven Modulo Sampling. ICASSP 2021: 5435-5439 - [i5]Dorian Florescu, Felix Krahmer, Ayush Bhandari:
The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments. CoRR abs/2111.12675 (2021) - 2020
- [c5]Dorian Florescu, Matthew England:
A Machine Learning Based Software Pipeline to Pick the Variable Ordering for Algorithms with Polynomial Inputs. ICMS 2020: 302-311 - [i4]Dorian Florescu, Matthew England:
A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs. CoRR abs/2005.11251 (2020)
2010 – 2019
- 2019
- [j3]Dorian Florescu, Daniel Coca:
Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines. Neural Comput. 31(9): 1825-1852 (2019) - [c4]Dorian Florescu, Matthew England:
Improved Cross-Validation for Classifiers that Make Algorithmic Choices to Minimise Runtime Without Compromising Output Correctness. MACIS 2019: 341-356 - [c3]Matthew England, Dorian Florescu:
Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple. MC 2019: 330-333 - [c2]Matthew England, Dorian Florescu:
Comparing Machine Learning Models to Choose the Variable Ordering for Cylindrical Algebraic Decomposition. CICM 2019: 93-108 - [i3]Matthew England, Dorian Florescu:
Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition. CoRR abs/1904.11061 (2019) - [i2]Dorian Florescu, Matthew England:
Algorithmically generating new algebraic features of polynomial systems for machine learning. CoRR abs/1906.01455 (2019) - [i1]Dorian Florescu, Matthew England:
Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness. CoRR abs/1911.12672 (2019) - 2018
- [j2]Dorian Florescu, Daniel Coca:
Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data. Neural Comput. 30(3) (2018) - 2015
- [j1]Dorian Florescu, Daniel Coca:
A Novel Reconstruction Framework for Time-Encoded Signals with Integrate-and-Fire Neurons. Neural Comput. 27(9): 1872-1898 (2015) - [c1]Dorian Florescu, Daniel Coca:
Implementation of linear filters in the spike domain. ECC 2015: 2298-2302
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