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Dorian Florescu

Orcid: 0000-0002-1169-674X

According to our database1, Dorian Florescu authored at least 19 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems.
[BibTeX]
[DOI]
Dorian Florescu
,
Matthew England
CoRR, 2024

2023
Unlimited Sampling of Bandpass Signals: Computational Demodulation via Undersampling.
[BibTeX]
[DOI]
Gal Shtendel
,
Dorian Florescu
,
Ayush Bhandari
IEEE Trans. Signal Process., 2023

2022
The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments.
[BibTeX]
[DOI]
Dorian Florescu
,
Felix Krahmer
,
Ayush Bhandari
IEEE Trans. Signal Process., 2022

Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation.
[BibTeX]
[DOI]
Dorian Florescu
,
Ayush Bhandari
IEEE Trans. Signal Process., 2022

Multi-Dimensional Unlimited Sampling and Robust Reconstruction.
[BibTeX]
[DOI]
Dorian Florescu
,
Ayush Bhandari
CoRR, 2022

Unlimited Sampling via Generalized Thresholding.
[BibTeX]
[DOI]
Dorian Florescu
,
Ayush Bhandari
Proceedings of the IEEE International Symposium on Information Theory, 2022

Modulo Event-Driven Sampling: System Identification and Hardware Experiments.
[BibTeX]
[DOI]
Dorian Florescu
,
Ayush Bhandari
Proceedings of the IEEE International Conference on Acoustics, 2022

Unlimited Sampling with Local Averages.
[BibTeX]
[DOI]
Dorian Florescu
,
Ayush Bhandari
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
Event-Driven Modulo Sampling.
[BibTeX]
[DOI]
Dorian Florescu
,
Felix Krahmer
,
Ayush Bhandari
Proceedings of the IEEE International Conference on Acoustics, 2021

Unlimited Sampling with Hysteresis.
[BibTeX]
[DOI]
Dorian Florescu
,
Felix Krahmer
,
Ayush Bhandari
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
A Machine Learning Based Software Pipeline to Pick the Variable Ordering for Algorithms with Polynomial Inputs.
[BibTeX]
[DOI]
Dorian Florescu
,
Matthew England
Proceedings of the Mathematical Software - ICMS 2020, 2020

2019
Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines.
[BibTeX]
[DOI]
Dorian Florescu
,
Daniel Coca
Neural Comput., 2019

Algorithmically Generating New Algebraic Features of Polynomial Systems for Machine Learning.
[BibTeX]
[DOI]
Dorian Florescu
,
Matthew England
Proceedings of the 4th SC-Square Workshop co-located with the SIAM Conference on Applied Algebraic Geometry, 2019

Comparing Machine Learning Models to Choose the Variable Ordering for Cylindrical Algebraic Decomposition.
[BibTeX]
[DOI]
Matthew England
,
Dorian Florescu
Proceedings of the Intelligent Computer Mathematics - 12th International Conference, 2019

Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple.
[BibTeX]
[DOI]
Matthew England
,
Dorian Florescu
Proceedings of the Maple in Mathematics Education and Research - Third Maple Conference, 2019

Improved Cross-Validation for Classifiers that Make Algorithmic Choices to Minimise Runtime Without Compromising Output Correctness.
[BibTeX]
[DOI]
Dorian Florescu
,
Matthew England
Proceedings of the Mathematical Aspects of Computer and Information Sciences, 2019

2018
Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data.
[BibTeX]
[DOI]
Dorian Florescu
,
Daniel Coca
Neural Comput., 2018

2015
A Novel Reconstruction Framework for Time-Encoded Signals with Integrate-and-Fire Neurons.
[BibTeX]
[DOI]
Dorian Florescu
,
Daniel Coca
Neural Comput., 2015

Implementation of linear filters in the spike domain.
[BibTeX]
[DOI]
Dorian Florescu
,
Daniel Coca
Proceedings of the 14th European Control Conference, 2015


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