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Found 8 papers, 2 papers with code
($\boldsymbolθ_l, \boldsymbolθ_u$)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces
Multi-task optimization is typically characterized by a fixed and finite set of optimization tasks.
Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models
Existing defense strategies are well equipped to thwart such attacks through backdoor detection and trigger inversion because previous attack methods are constrained by limited input spaces and low-dimensional triggers.
Evolutionary Optimization of Physics-Informed Neural Networks: Survey and Prospects
Methods synergizing gradient descent and neuroevolution for discovering bespoke neural architectures and balancing multiple conflicting terms in physics-informed learning objectives are positioned as important avenues for future research.
Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons
In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code.
LLM2FEA: Discover Novel Designs with Generative Evolutionary Multitasking
This raises the intriguing possibility of using generative models to simultaneously tackle design tasks across multiple domains, facilitating cross-domain learning and resulting in a series of innovative design solutions.
Precise-Physics Driven Text-to-3D Generation
However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes.
Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization
In this paper, we introduce a novel concept of \textit{inverse transfer} in multiobjective optimization.
CS-PCN: Context-Space Progressive Collaborative Network for Image Denoising
Currently, image-denoising methods based on deep learning cannot adequately reconcile contextual semantic information and spatial details.
