Zobrazeno 1 - 10
of 4 222
pro vyhledávání: '"A. Hoehne"'
Heatmaps generated on inputs of image classification networks via explainable AI methods like Grad-CAM and LRP have been observed to resemble segmentations of input images in many cases. Consequently, heatmaps have also been leveraged for achieving w
Externí odkaz:
http://arxiv.org/abs/2407.03009
Autor:
Kopf, Laura, Bommer, Philine Lou, Hedström, Anna, Lapuschkin, Sebastian, Höhne, Marina M. -C., Bykov, Kirill
A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is the ability to explain learned concepts within their latent representations. While various methods exist to connect neurons to textual descriptions of human-unders
Externí odkaz:
http://arxiv.org/abs/2405.20331
The Model Parameter Randomisation Test (MPRT) is highly recognised in the eXplainable Artificial Intelligence (XAI) community due to its fundamental evaluative criterion: explanations should be sensitive to the parameters of the model they seek to ex
Externí odkaz:
http://arxiv.org/abs/2405.02383
We revisit the stability of the Standard Model vacuum, and investigate its quantum effective potential using the highest available orders in perturbation theory and the most accurate determination of input parameters to date. We observe that the stab
Externí odkaz:
http://arxiv.org/abs/2401.08811
The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the eXplainable Artificial Intelligence (XAI) community for its well-motivated evaluative principle: that the explanation function should be sensitive to changes in the parameter
Externí odkaz:
http://arxiv.org/abs/2401.06465
Autor:
Bareeva, Dilyara, Höhne, Marina M. -C., Warnecke, Alexander, Pirch, Lukas, Müller, Klaus-Robert, Rieck, Konrad, Bykov, Kirill
Deep Neural Networks (DNNs) are capable of learning complex and versatile representations, however, the semantic nature of the learned concepts remains unknown. A common method used to explain the concepts learned by DNNs is Feature Visualization (FV
Externí odkaz:
http://arxiv.org/abs/2401.06122
Autor:
Liu, Shanghua, Hedström, Anna, Basavegowda, Deepak Hanike, Weltzien, Cornelia, Höhne, Marina M. -C.
Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacles to large-scale grassland monitoring. These challenges stem from the
Externí odkaz:
http://arxiv.org/abs/2312.08408
Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on the other hand, self-explainable models (SEMs) which are trained direc
Externí odkaz:
http://arxiv.org/abs/2312.07822
Publikováno v:
37th Conference on Neural Information Processing Systems (NeurIPS 2023)
Deep Neural Networks (DNNs) demonstrate remarkable capabilities in learning complex hierarchical data representations, but the nature of these representations remains largely unknown. Existing global explainability methods, such as Network Dissection
Externí odkaz:
http://arxiv.org/abs/2311.13594
Autor:
Schmidt, Jack-Andre, Schmidt, Bernd, Falter, Jens, Hoehne, Jens, Savio, Claudio Dal, Schaile, Sebatsian, Schirmeisen, Andre
Here we report the performance of a small scale 4 K pulse tube cryocooler operating with a low input power reaching a minimum temperature of 2.2 K, as well as a cooling capacity of over 240 mW at 4.2 K. The compressor is air cooled and can be supplie
Externí odkaz:
http://arxiv.org/abs/2311.00605