Results 41 to 50 of about 107,198 (316)

Wasserstein Adversarial Robustness [PDF]

, 2020
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks.
Wu, Kaiwen
core  

learningmatter-mit/Atomistic-Adversarial-Attacks: Paper publication version

, 2021
This release contains the data, models, and scripts to reproduce our paper "Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks"
Daniel Schwalbe-Koda
core   +1 more source

Probabilistic Categorical Adversarial Attack & Adversarial Training

CoRR, 2022
The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han, He, Pengfei, Ren, Jie, Wan, Yuxuan, Liu, Zitao, Liu, Hui, Tang, Jiliang   +6 more
openaire   +2 more sources

INVESTIGATING JUDGE UNDER THE FRENCH CODE OF CRIMINAL PROCEDURE

Правовое государство: теория и практика
The investigating judge was first mentioned in the French Code of Criminal Procedure in 1808, and then in other countries, including Russia, where a similar procedural person was called a court investigator and was abolished in the early years of Soviet
BELONOSOV Vladimir Olegovych
doaj   +1 more source

Adversarial AutoAugment

CoRR, 2019
Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks. Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy. Through finding the best policy in well-designed search space of data augmentation, AutoAugment can significantly improve ...
Xinyu Zhang, Qiang Wang, Jian Zhang, Zhao Zhong   +3 more
openaire   +3 more sources

Adversarial Gain

CoRR, 2018
Adversarial examples can be defined as inputs to a model which induce a mistake - where the model output is different than that of an oracle, perhaps in surprising or malicious ways. Original models of adversarial attacks are primarily studied in the context of classification and computer vision tasks.
Peter Henderson 0002, Koustuv Sinha, Nan Rosemary Ke, Joelle Pineau   +3 more
openaire   +2 more sources

Adversarial Thresholding Semi-Bandits [PDF]

, 2020
The classical multi-armed bandit is one of the most common examples of sequential decision-making, either by trading-off between exploiting and exploring arms to maximise some payoff or purely exploring arms until the optimal arm is identified.
Bower, Craig
core  

Confrontation and the Criminal Defendant in a Hybrid Legal System: The Republic of North Macedonia

SEEU Review
This note analyzes the treatment of out-of-court statements in the Republic of North Macedonia’s (NMK) hybrid criminal procedure system, which blends adversarial and neoinquisitorial elements.
Siegel David M.
doaj   +1 more source

Adversarial Spheres

CoRR, 2018
State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image.
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow   +6 more
openaire   +3 more sources

Adversarial attacks and adversarial robustness in computational pathology. [PDF]

, 2022
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks.
van Treeck, Marko, Buelow, Roman D., Ghaffari Laleh, Narmin, Veldhuizen, Gregory Patrick, Dislich, Bastian, Boor, Peter, Han, Tianyu, Buelow, Roman D, Treeck, Marko van, Schulz, Volkmar, Buelow, Roman David, Truhn, Daniel, Langer, Rupert, Kather, Jakob Nikolas   +13 more
core   +1 more source