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Akademický článek
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Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This
Externí odkaz:
http://arxiv.org/abs/2410.23879
We propose a novel feature-centric approach to surrogate modeling of dynamical systems driven by time-varying exogenous excitations. This approach, named Functional Nonlinear AutoRegressive with eXogenous inputs (F-NARX), aims to approximate the syst
Externí odkaz:
http://arxiv.org/abs/2410.07293
Autor:
Breuer, Timo, Kreutz, Christin Katharina, Fuhr, Norbert, Balog, Krisztian, Schaer, Philipp, Bernard, Nolwenn, Frommholz, Ingo, Gohsen, Marcel, Ji, Kaixin, Jones, Gareth J. F., Keller, Jüri, Liu, Jiqun, Mladenov, Martin, Pasi, Gabriella, Trippas, Johanne, Wang, Xi, Zerhoudi, Saber, Zhai, ChengXiang
This paper is a report of the Workshop on Simulations for Information Access (Sim4IA) workshop at SIGIR 2024. The workshop had two keynotes, a panel discussion, nine lightning talks, and two breakout sessions. Key takeaways were user simulation's imp
Externí odkaz:
http://arxiv.org/abs/2409.18024
Information Retrieval (IR) systems are exposed to constant changes in most components. Documents are created, updated, or deleted, the information needs are changing, and even relevance might not be static. While it is generally expected that the IR
Externí odkaz:
http://arxiv.org/abs/2409.05417
Autor:
Esther Wiesner
Publikováno v:
Schweizerische Zeitschrift für Bildungswissenschaften, Vol 44, Iss 2 (2022)
Rezension
Externí odkaz:
https://doaj.org/article/a4193dc594434fd9a82c73aaf2649602
Autor:
Storz, Simon, Kulikov, Anatoly, Schär, Josua D., Barizien, Victor, Valcarce, Xavier, Berterottière, Florence, Sangouard, Nicolas, Bancal, Jean-Daniel, Wallraff, Andreas
Self-testing protocols enable the certification of quantum systems in a device-independent manner, i.e. without knowledge of the inner workings of the quantum devices under test. Here, we demonstrate this high standard for characterization routines w
Externí odkaz:
http://arxiv.org/abs/2408.01299
Autor:
Bui, Anh Thu Maria, Brech, Saskia Felizitas, Hußfeldt, Natalie, Jennert, Tobias, Ullrich, Melanie, Breuer, Timo, Khasmakhi, Narjes Nikzad, Schaer, Philipp
Hallucination detection in Large Language Models (LLMs) is crucial for ensuring their reliability. This work presents our participation in the CLEF ELOQUENT HalluciGen shared task, where the goal is to develop evaluators for both generating and detec
Externí odkaz:
http://arxiv.org/abs/2407.09152
Publikováno v:
Proceedings of the 2024 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR '24), July 13, 2024, Washington, DC, USA
Information retrieval systems have been evaluated using the Cranfield paradigm for many years. This paradigm allows a systematic, fair, and reproducible evaluation of different retrieval methods in fixed experimental environments. However, real-world
Externí odkaz:
http://arxiv.org/abs/2407.01373
Autor:
Spagnolo, Federico, Molchanova, Nataliia, Schaer, Roger, Cuadra, Meritxell Bach, Pineda, Mario Ocampo, Melie-Garcia, Lester, Granziera, Cristina, Andrearczyk, Vincent, Depeursinge, Adrien
In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models' decision mechanisms in the case of classification tasks. However, XAI for semantic segmentation and in particular for single instances ha
Externí odkaz:
http://arxiv.org/abs/2406.09335