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pro vyhledávání: '"Roberts , Jay"'
Autor:
Tsiligkaridis, Theodoros, Roberts, Jay
Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique that approximately solves a robust optimization problem to minimize the worst-case loss and is widely regarded as the
Externí odkaz:
http://arxiv.org/abs/2012.12368
Autor:
Roberts, Jay, Tsiligkaridis, Theodoros
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used
Externí odkaz:
http://arxiv.org/abs/2012.01145
Autor:
Tsiligkaridis, Theodoros, Roberts, Jay
Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique aimed at learning features robust to such attacks and is widely regarded as a very effective defense. However, the co
Externí odkaz:
http://arxiv.org/abs/2009.04923
The affine motion of two-dimensional (2d) incompressible fluids surrounded by vacuum can be reduced to a completely integrable and globally solvable Hamiltonian system of ordinary differential equations for the deformation gradient in ${\rm SL}(2,{\m
Externí odkaz:
http://arxiv.org/abs/1811.07781
We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging high-frequ
Externí odkaz:
http://arxiv.org/abs/1810.06610
Publikováno v:
In Journal of Computational Physics 15 May 2020 409
Publikováno v:
BJUI Compass; Apr2024, Vol. 5 Issue 4, p460-465, 6p