Results 81 to 90 of about 3,499,798 (281)
Multiple Adversarial Domains Adaptation Approach for Mitigating Adversarial Attacks Effects
International Transactions on Electrical Energy Systems, 2022 Although neural networks are near achieving performance similar to humans in many tasks, they are susceptible to adversarial attacks in the form of a small, intentionally designed perturbation, which could lead to misclassifications.
Bader Rasheed +4 more
doaj +1 more source
Domain Generalization via Adversarially Learned Novel Domains
IEEE Access, 2022 This study focuses on the domain generalization task, which aims to learn a model that generalizes to unseen domains by utilizing multiple training domains.
Yu Zhe +3 more
doaj +1 more source
On the Convergence and Robustness of Adversarial Training
CoRR, 2021 Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques, adversarial training with Projected Gradient Decent (PGD) is amongst the most effective.
Yisen Wang 0001 +5 more
openaire +3 more sources
Learning Graph Embedding With Adversarial Training Methods
, 2020 Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction ...
Jiang, Jing +5 more
core +1 more source
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks
Existing self-supervised adversarial training (self-AT) methods rely on hand-crafted adversarial attack strategies for PGD attacks, which fail to adapt to the evolving learning dynamics of the model and do not account for instance-specific ...
Saini, Rajkumar +5 more
core +2 more sources
Research on adversarial attack and defense of photovoltaic power prediction
Dianzi Jishu YingyongDeep neural networks have been widely used in photovoltaic power prediction, but they are vulnerable to adversarial attacks. In order to improve the robustness of the prediction model, an adversarial attack algorithm based on fast gradient sign method ...
Zhou Wang
doaj +1 more source
Local Reweighting for Adversarial Training
CoRR, 2021 Instances-reweighted adversarial training (IRAT) can significantly boost the robustness of trained models, where data being less/more vulnerable to the given attack are assigned smaller/larger weights during training. However, when tested on attacks different from the given attack simulated in training, the robustness may drop significantly (e.g., even
Ruize Gao +5 more
openaire +2 more sources
Advanced Science, EarlyView.Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li +6 more
wiley +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source