Results 91 to 100 of about 1,662,189 (295)
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
Interpreting Adversarial Examples with Attributes
CoRR, 2019 Deep computer vision systems being vulnerable to imperceptible and carefully crafted noise have raised questions regarding the robustness of their decisions. We take a step back and approach this problem from an orthogonal direction. We propose to enable black-box neural networks to justify their reasoning both for clean and for adversarial examples by
Sadaf Gulshad +3 more
openaire +3 more sources
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
On the (Statistical) Detection of Adversarial Examples
CoRR, 2017 Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or Malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model.
Kathrin Grosse +4 more
openaire +2 more sources
Defending Neural Networks Against Adversarial Examples [PDF]
, 2018 Deep learning is becoming a technology central to the safety of cars, the security of networks, and the correct functioning of many other types of systems. Unfortunately, attackers can create adversarial examples, small perturbations to inputs that trick
Barton, Armon
core +1 more source
Advanced Science, EarlyView.A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley +1 more source
Human-Producible Adversarial Examples
, 2023 Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers to be produced in the physical real world.
Fawaz, Kassem +5 more
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Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification
IEEE Access, 2020 Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis.
Li Chen +7 more
doaj +1 more source
Advanced Science, EarlyView.Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source
Generative adversarial networks for generating adversarial examples
, 2022 Suvremeni klasifikacijski modeli, unatoč zavidnim rezultatima na nizu skupova podataka, i dalje znaju biti podložni napadima neprijateljskim uzorcima. Jedan od načina oblikovanja tih ciljano izmijenjenih uzoraka oslanja se na generativne suparničke mreže.
Dujmović, Bruna
core