Zobrazeno 1 - 10
of 497
pro vyhledávání: '"RAUBER, ANDREAS"'
There are settings in which reproducibility of ranked lists is desirable, such as when extracting a subset of an evolving document corpus for downstream research tasks or in domains such as patent retrieval or in medical systematic reviews, with high
Externí odkaz:
http://arxiv.org/abs/2411.04051
Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generated content. As these models often train on publicly available data, inc
Externí odkaz:
http://arxiv.org/abs/2406.15386
Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users ability to predict the mode
Externí odkaz:
http://arxiv.org/abs/2309.11987
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2023
The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains t
Externí odkaz:
http://arxiv.org/abs/2304.11285
Publikováno v:
ACM Computing Surveys, 2023
Machine Learning-as-a-Service (MLaaS) has become a widespread paradigm, making even the most complex machine learning models available for clients via e.g. a pay-per-query principle. This allows users to avoid time-consuming processes of data collect
Externí odkaz:
http://arxiv.org/abs/2206.08451
Autor:
Landauer, Max, Skopik, Florian, Frank, Maximilian, Hotwagner, Wolfgang, Wurzenberger, Markus, Rauber, Andreas
Publikováno v:
IEEE Transactions on Dependable and Secure Computing (2022)
Intrusion detection systems (IDS) monitor system logs and network traffic to recognize malicious activities in computer networks. Evaluating and comparing IDSs with respect to their detection accuracies is thereby essential for their selection in spe
Externí odkaz:
http://arxiv.org/abs/2203.08580
We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples and metadata as input. The audio is fed into a Convolutional Neural Network (CNN) using four convolutional layer
Externí odkaz:
http://arxiv.org/abs/1811.04448
In this paper we present a Deep Neural Network architecture for the task of acoustic scene classification which harnesses information from increasing temporal resolutions of Mel-Spectrogram segments. This architecture is composed of separated paralle
Externí odkaz:
http://arxiv.org/abs/1811.04419
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; October 2024, Vol. 35 Issue: 10 p13082-13100, 19p
Publikováno v:
In Computers & Security May 2020 92