16,905 research outputs found
Analyses of celestial pole offsets with VLBI, LLR, and optical observations
This work aims to explore the possibilities of determining the long-period
part of the precession-nutation of the Earth with techniques other than very
long baseline interferometry (VLBI). Lunar laser ranging (LLR) is chosen for
its relatively high accuracy and long period. Results of previous studies could
be updated using the latest data with generally higher quality, which would
also add ten years to the total time span. Historical optical data are also
analyzed for their rather long time-coverage to determine whether it is
possible to improve the current Earth precession-nutation model
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
Deep neural networks have been found vulnerable to adversarial attacks, thus
raising potentially concerns in security-sensitive contexts. To address this
problem, recent research has investigated the adversarial robustness of deep
neural networks from the architectural point of view. However, searching for
architectures of deep neural networks is computationally expensive,
particularly when coupled with adversarial training process. To meet the above
challenge, this paper proposes a bi-fidelity multiobjective neural architecture
search approach. First, we formulate the NAS problem for enhancing adversarial
robustness of deep neural networks into a multiobjective optimization problem.
Specifically, in addition to a low-fidelity performance predictor as the first
objective, we leverage an auxiliary-objective -- the value of which is the
output of a surrogate model trained with high-fidelity evaluations. Secondly,
we reduce the computational cost by combining three performance estimation
methods, i.e., parameter sharing, low-fidelity evaluation, and surrogate-based
predictor. The effectiveness of the proposed approach is confirmed by extensive
experiments conducted on CIFAR-10, CIFAR-100 and SVHN datasets
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