Zobrazeno 1 - 10
of 27 850
pro vyhledávání: '"Kessel, A"'
Autor:
Kessel, Marcus
Empirical software engineering faces a critical gap: the lack of standardized tools for rapid development and execution of Test-Driven Software Experiments (TDSEs) - that is, experiments that involve the execution of software subjects and the observa
Externí odkaz:
http://arxiv.org/abs/2410.08911
Autor:
Kessel, Marcus, Atkinson, Colin
Publikováno v:
IEEE Software September 2024
Generative AI (GAI) holds great potential to improve software engineering productivity, but its untrustworthy outputs, particularly in code synthesis, pose significant challenges. The need for extensive verification and validation (V&V) of GAI-genera
Externí odkaz:
http://arxiv.org/abs/2409.14071
Autor:
van Kessel, Marcel
This is a paper in the field of ontological deterministic theories behind Quantum Field Theories, like for example the cellular automaton theories proposed by 't Hooft. In these theories one has ontological states in which the state of reality is exa
Externí odkaz:
http://arxiv.org/abs/2407.13799
Equivariant neural networks have in recent years become an important technique for guiding architecture selection for neural networks with many applications in domains ranging from medical image analysis to quantum chemistry. In particular, as the mo
Externí odkaz:
http://arxiv.org/abs/2406.06504
Autor:
Nicoli, Kim A., Anders, Christopher J., Funcke, Lena, Hartung, Tobias, Jansen, Karl, Kühn, Stefan, Müller, Klaus-Robert, Stornati, Paolo, Kessel, Pan, Nakajima, Shinichi
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian. Specifically, we
Externí odkaz:
http://arxiv.org/abs/2406.06150
Autor:
Kessel, Marcus, Atkinson, Colin
The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of
Externí odkaz:
http://arxiv.org/abs/2406.04710
Autor:
Unterauer, Arnold, Bucher, David, Knoll, Matthias, Economides, Constantin, Lachner, Michael, Germain, Thomas, Kessel, Moritz, Hajdinovic, Smajo, Stein, Jonas
Publikováno v:
GECCO '24 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2024
Quantum computing has the potential for disruptive change in many sectors of industry, especially in materials science and optimization. In this paper, we describe how the Turbine Balancing Problem can be solved with quantum computing, which is the N
Externí odkaz:
http://arxiv.org/abs/2405.06412
Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training. However, they are often prohibitively more expensive from a computati
Externí odkaz:
http://arxiv.org/abs/2403.15881
Autor:
Gerken, Jan E., Kessel, Pan
We show that deep ensembles become equivariant for all inputs and at all training times by simply using data augmentation. Crucially, equivariance holds off-manifold and for any architecture in the infinite width limit. The equivariance is emergent i
Externí odkaz:
http://arxiv.org/abs/2403.03103
In silico screening uses predictive models to select a batch of compounds with favorable properties from a library for experimental validation. Unlike conventional learning paradigms, success in this context is measured by the performance of the pred
Externí odkaz:
http://arxiv.org/abs/2307.09379