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2020 – today
- 2024
[c6]Sima Sinaei, Mohammadreza Mohammadi, Rakesh Shrestha, Mina Alibeigi, David Eklund:
PRIV-DRIVE: Privacy-Ensured Federated Learning using Homomorphic Encryption for Driver Fatigue Detection. DSD 2024: 427-434- [c5]David Eklund, Alfonso Iacovazzi, Han Wang, Apostolos Pyrgelis, Shahid Raza:
BMI: Bounded Mutual Information for Efficient Privacy-Preserving Feature Selection. ESORICS (2) 2024: 353-373 - 2023
- [j12]David Eklund:
The numerical algebraic geometry of bottlenecks. Adv. Appl. Math. 142: 102416 (2023) - [j11]Han Wang
, Luis Muñoz-González, Muhammad Zaid Hameed, David Eklund, Shahid Raza:
SparSFA: Towards robust and communication-efficient peer-to-peer federated learning. Comput. Secur. 129: 103182 (2023) - [j10]Sandra Di Rocco, Parker B. Edwards, David Eklund, Oliver Gäfvert, Jonathan D. Hauenstein:
Computing Geometric Feature Sizes for Algebraic Manifolds. SIAM J. Appl. Algebra Geom. 7(4): 716-741 (2023) - [j9]Han Wang, David Eklund, Alina Oprea, Shahid Raza:
FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning. ACM Trans. Internet Things 4(3): 17:1-17:24 (2023) - [j8]Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg:
Identifying latent distances with Finslerian geometry. Trans. Mach. Learn. Res. 2023 (2023) - [c4]Mohammadreza Mohammadi, Roberto Allocca, David Eklund, Rakesh Shrestha, Sima Sinaei:
Privacy-preserving Federated Learning System for Fatigue Detection. CSR 2023: 624-629 - 2022
- [j7]Sandra Di Rocco, David Eklund, Oliver Gäfvert:
Sampling and homology via bottlenecks. Math. Comput. 91(338): 2969-2995 (2022) - [i4]Sandra Di Rocco, Parker B. Edwards, David Eklund, Oliver Gäfvert, Jonathan D. Hauenstein:
Computing geometric feature sizes for algebraic manifolds. CoRR abs/2209.01654 (2022) - [i3]Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg:
Identifying latent distances with Finslerian geometry. CoRR abs/2212.10010 (2022) - 2021
- [c3]Daniel J. Bates, David Eklund, Jonathan D. Hauenstein, Chris Peterson:
Excess intersections and numerical irreducible decompositions. SYNASC 2021: 52-60 - [c2]Han Wang, Luis Muñoz-González, David Eklund, Shahid Raza:
Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection. WISEC 2021: 153-163 - 2020
- [j6]Sandra Di Rocco, David Eklund, Madeleine Weinstein:
The Bottleneck Degree of Algebraic Varieties. SIAM J. Appl. Algebra Geom. 4(1): 227-253 (2020) - [c1]Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg:
Variational Autoencoders with Riemannian Brownian Motion Priors. ICML 2020: 5053-5066 - [i2]Dimitris Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg:
Variational Autoencoders with Riemannian Brownian Motion Priors. CoRR abs/2002.05227 (2020)
2010 – 2019
- 2019
- [i1]David Eklund, Søren Hauberg:
Expected path length on random manifolds. CoRR abs/1908.07377 (2019) - 2018
- [j5]Sandra Di Rocco, David Eklund, Chris Peterson:
Numerical polar calculus and cohomology of line bundles. Adv. Appl. Math. 100: 148-162 (2018) - 2014
- [j4]Daniel J. Bates, Brent R. Davis, David Eklund, Eric M. Hanson, Chris Peterson:
Perturbed homotopies for finding all isolated solutions of polynomial systems. Appl. Math. Comput. 247: 301-311 (2014) - 2013
- [j3]Daniel J. Bates, David Eklund, Chris Peterson:
Computing intersection numbers of Chern classes. J. Symb. Comput. 50: 493-507 (2013) - 2011
- [j2]Sandra Di Rocco, David Eklund, Chris Peterson, Andrew J. Sommese:
Chern numbers of smooth varieties via homotopy continuation and intersection theory. J. Symb. Comput. 46(1): 23-33 (2011) - 2010
- [j1]Sandra Di Rocco, David Eklund, Andrew J. Sommese, Charles W. Wampler:
Algebraic C*-actions and the inverse kinematics of a general 6R manipulator. Appl. Math. Comput. 216(9): 2512-2524 (2010)
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