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Thomas Bäck

8 papers hereh-index 691 citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.LG2
  • cs.ET1
  • cs.NE1
  • quant-ph1
same name
  • Thomas Bäck — 18 papers, h 6
  • Thomas Bäck — 2 papers, h 1
  • Thomas Bäck — 2 papers, h 5
  • Thomas Bäck — 1 paper, h 2
  • Thomas Bäck — 1 paper
  • Thomas Bäck — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing 2025Show all

4 papers · 1 filter

cs.ET2025

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…

cs.LG2025

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…

cs.LG2025

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…

cs.CV2025

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.