Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Anders, Christopher J."'
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:
Bender, Sidney, Anders, Christopher J., Chormai, Pattarawatt, Marxfeld, Heike, Herrmann, Jan, Montavon, Grégoire
This paper introduces a novel technique called counterfactual knowledge distillation (CFKD) to detect and remove reliance on confounders in deep learning models with the help of human expert feedback. Confounders are spurious features that models ten
Externí odkaz:
http://arxiv.org/abs/2310.01011
Autor:
Dreyer, Maximilian, Pahde, Frederik, Anders, Christopher J., Samek, Wojciech, Lapuschkin, Sebastian
Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions. This poses risks when deploying these models for high-stake decision-making, such as in medical applications. C
Externí odkaz:
http://arxiv.org/abs/2308.09437
Autor:
Nicoli, Kim A., Anders, Christopher J., Hartung, Tobias, Jansen, Karl, Kessel, Pan, Nakajima, Shinichi
We study the consequences of mode-collapse of normalizing flows in the context of lattice field theory. Normalizing flows allow for independent sampling. For this reason, it is hoped that they can avoid the tunneling problem of local-update MCMC algo
Externí odkaz:
http://arxiv.org/abs/2302.14082
Autor:
Pahde, Frederik, Dreyer, Maximilian, Weber, Leander, Weckbecker, Moritz, Anders, Christopher J., Wiegand, Thomas, Samek, Wojciech, Lapuschkin, Sebastian
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable concepts in the latent space. Commonly, CAVs are computed by leveraging
Externí odkaz:
http://arxiv.org/abs/2202.03482
Autor:
Anders, Christopher J., Neumann, David, Samek, Wojciech, Müller, Klaus-Robert, Lapuschkin, Sebastian
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence (XAI), approaches are available to explore the reasoning behind those
Externí odkaz:
http://arxiv.org/abs/2106.13200
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible to manipulations. With this work, we aim to enhance the resilience of e
Externí odkaz:
http://arxiv.org/abs/2012.10425
Autor:
Anders, Christopher J., Pasliev, Plamen, Dombrowski, Ann-Kathrin, Müller, Klaus-Robert, Kessel, Pan
Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making process of the algorithm and thereby increase its acceptance by the e
Externí odkaz:
http://arxiv.org/abs/2007.09969
Autor:
Nicoli, Kim A., Anders, Christopher J., Funcke, Lena, Hartung, Tobias, Jansen, Karl, Kessel, Pan, Nakajima, Shinichi, Stornati, Paolo
Publikováno v:
Phys. Rev. Lett. 126, 032001 (2021)
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, we show that gener
Externí odkaz:
http://arxiv.org/abs/2007.07115
Autor:
Samek, Wojciech, Montavon, Grégoire, Lapuschkin, Sebastian, Anders, Christopher J., Müller, Klaus-Robert
With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI. Interpretability and explanation methods for gaining a better understanding about the problem solving a
Externí odkaz:
http://arxiv.org/abs/2003.07631