Zobrazeno 1 - 10
of 699
pro vyhledávání: '"Deep taylor decomposition"'
Autor:
Sixt, Leon, Landgraf, Tim
Saliency methods attempt to explain deep neural networks by highlighting the most salient features of a sample. Some widely used methods are based on a theoretical framework called Deep Taylor Decomposition (DTD), which formalizes the recursive appli
Externí odkaz:
http://arxiv.org/abs/2211.08425
We argue that we need to evaluate model interpretability methods 'in the wild', i.e., in situations where professionals make critical decisions, and models can potentially assist them. We present an in-the-wild evaluation of token attribution based o
Externí odkaz:
http://arxiv.org/abs/2206.02661
A common machine learning task is to discriminate between normal and anomalous data points. In practice, it is not always sufficient to reach high accuracy at this task, one also would like to understand why a given data point has been predicted in a
Externí odkaz:
http://arxiv.org/abs/1805.06230
Publikováno v:
In Pattern Recognition May 2020 101
Autor:
Montavon, Grégoire, Bach, Sebastian, Binder, Alexander, Samek, Wojciech, Müller, Klaus-Robert
Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processing or human action recognition. Although these methods perform impress
Externí odkaz:
http://arxiv.org/abs/1512.02479
Autor:
Montavon, Grégoire, Lapuschkin, Sebastian, Binder, Alexander, Samek, Wojciech, Müller, Klaus-Robert
Publikováno v:
In Pattern Recognition May 2017 65:211-222
Conference
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Publikováno v:
Journal of Materials Research and Technology, Vol 25, Iss , Pp 273-284 (2023)
Accurately identifying the high-temperature history experienced by rocks is essential for understanding their behaviour and predicting properties. However, current approaches are limited by the heterogeneity of rocks, test scale and costs. Here, we p
Externí odkaz:
https://doaj.org/article/01823ef74cfa40fca2c1dfcfcc23406a
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030332457
OTM Conferences
OTM Conferences
In this work, we present iDropout, a new method to adjust dropout, from purely randomly dropping inputs to dropping inputs based on a mix based on the relevance of the nodes and some randomness. We use Deep Taylor Decomposition to calculate the respe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e38c1c595e5c21ef3e6f8a7d98143c2e
https://doi.org/10.1007/978-3-030-33246-4_7
https://doi.org/10.1007/978-3-030-33246-4_7
Publikováno v:
Pattern Recognition. 101:107198
Detecting anomalies in the data is a common machine learning task, with numerous applications in the sciences and industry. In practice, it is not always sufficient to reach high detection accuracy, one would also like to be able to understand why a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7232308dd8358c6f193eaab2f801bb4
https://doi.org/10.1016/j.patcog.2020.107198
https://doi.org/10.1016/j.patcog.2020.107198