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Found 8 papers, 0 papers with code
Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization
In recent years, despite significant advancements in adversarial attack research, the security challenges in cross-modal scenarios, such as the transferability of adversarial attacks between infrared, thermal, and RGB images, have been overlooked.
Phy124: Fast Physics-Driven 4D Content Generation from a Single Image
Secondly, the extensive sampling process and the large number of parameters in diffusion models result in exceedingly time-consuming generation processes.
Adversarial Learning for Neural PDE Solvers with Sparse Data
Neural network solvers for partial differential equations (PDEs) have made significant progress, yet they continue to face challenges related to data scarcity and model robustness.
Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
\textit{Attack} uses an evolutionary algorithm to attack the crucial regions, where the attacks are semantically related to the target texts of \textit{Ask}, thus achieving targeted attacks without semantic loss.
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models
Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science.
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
