5 papers
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
Shuaiqun Pan, Yash J. Patel, Aneta Neumann +3
Variational quantum algorithms, such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), have become increasingly popular, offering promising avenues for employing…
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models provide efficient alternatives to computationally demanding real world processes but often require large datasets for effective training. A promising solution to t…
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez +2
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often deman…
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependen…
A Mesh Is Worth 512 Numbers: Spectral-domain Diffusion Modeling for High-dimension Shape Generation
Jiajie Fan, Amal Trigui, Andrea Bonfanti +3
Recent advancements in learning latent codes derived from high-dimensional shapes have demonstrated impressive outcomes in 3D generative modeling. Traditionally, these approaches e…