Zobrazeno 1 - 10
of 1 863
pro vyhledávání: '"A. Kowald"'
Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacte
Externí odkaz:
http://arxiv.org/abs/2409.17769
Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training data, notably the US. However, it remains unclear to what extent feedback
Externí odkaz:
http://arxiv.org/abs/2408.11565
Autor:
Duricic, Tomislav, Müllner, Peter, Weidinger, Nicole, ElSayed, Neven, Kowald, Dominik, Veas, Eduardo
Many industrial sectors rely on well-trained employees that are able to operate complex machinery. In this work, we demonstrate an AI-powered immersive assistance system that supports users in performing complex tasks in industrial environments. Spec
Externí odkaz:
http://arxiv.org/abs/2407.09147
Autor:
Semmelrock, Harald, Ross-Hellauer, Tony, Kopeinik, Simone, Theiler, Dieter, Haberl, Armin, Thalmann, Stefan, Kowald, Dominik
Research in various fields is currently experiencing challenges regarding the reproducibility of results. This problem is also prevalent in machine learning (ML) research. The issue arises, for example, due to unpublished data and/or source code and
Externí odkaz:
http://arxiv.org/abs/2406.14325
Autor:
Escobedo, Gustavo, Moscati, Marta, Muellner, Peter, Kopeinik, Simone, Kowald, Dominik, Lex, Elisabeth, Schedl, Markus
Users' interaction or preference data used in recommender systems carry the risk of unintentionally revealing users' private attributes (e.g., gender or race). This risk becomes particularly concerning when the training data contains user preferences
Externí odkaz:
http://arxiv.org/abs/2406.11505
Autor:
Kowald, Dominik
Recommender systems have become a pervasive part of our daily online experience, and are one of the most widely used applications of artificial intelligence and machine learning. Therefore, regulations and requirements for trustworthy artificial inte
Externí odkaz:
http://arxiv.org/abs/2406.11323
Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is not well un
Externí odkaz:
http://arxiv.org/abs/2401.03883
aTrain is an open-source and offline tool for transcribing audio data in multiple languages with CPU and NVIDIA GPU support. It is specifically designed for researchers using qualitative data generated from various forms of speech interactions with r
Externí odkaz:
http://arxiv.org/abs/2310.11967
Autor:
Marciniak, A., Ďurech, J., Choukroun, A., Hanuš, J., Ogłoza, W., Szakáts, R., Molnár, L., Pál, A., Monteiro, F., Frappa, E., Beisker, W., Pavlov, H., Moore, J., Adomavičienė, R., Aikawa, R., Andersson, S., Antonini, P., Argentin, Y., Asai, A., Assoignon, P., Barton, J., Baruffetti, P., Bath, K. L., Behrend, R., Benedyktowicz, L., Bernasconi, L., Biguet, G., Billiani, M., Błażewicz, D., Boninsegna, R., Borkowski, M., Bosch, J., Brazill, S., Bronikowska, M., Bruno, A., Bąk, M. Butkiewicz, Caron, J., Casalnuovo, G., Castellani, J. J., Ceravolo, P., Conjat, M., Delincak, P., Delpau, J., Demeautis, C., Demirkol, A., Dróżdż, M., Duffard, R., Durandet, C., Eisfeldt, D., Evangelista, M., Fauvaud, S., Fauvaud, M., Ferrais, M., Filipek, M., Fini, P., Fukui, K., Gährken, B., Geier, S., George, T., Goffin, B., Golonka, J., Goto, T., Grice, J., Guhl, K., Halíř, K., Hanna, W., Harman, M., Hashimoto, A., Hasubick, W., Higgins, D., Higuchi, M., Hirose, T., Hirsch, R., Hofschulz, O., Horaguchi, T., Horbowicz, J., Ida, M., Ignácz, B., Ishida, M., Isobe, K., Jehin, E., Joachimczyk, B., Jones, A., Juan, J., Kamiński, K., Kamińska, M. K., Kankiewicz, P., Kasebe, H., Kattentidt, B., Kim, D. -H., Kim, M. -J., Kitazaki, K., Klotz, A., Komraus, M., Konstanciak, I., Tóth, R. Könyves, Kouno, K., Kowald, E., Krajewski, J., Krannich, G., Kreutzer, A., Kryszczyńska, A., Kubánek, J., Kudak, V., Kugel, F., Kukita, R., Kulczak, P., Lazzaro, D., Licandro, J., Livet, F., Maley, P., Manago, N., Mánek, J., Manna, A., Matsushita, H., Meister, S., Mesquita, W., Messner, S., Michelet, J., Michimani, J., Mieczkowska, I., Morales, N., Motyliński, M., Murawiecka, M., Newman, J., Nikitin, V., Nishimura, M., Oey, J., Oszkiewicz, D., Owada, M., Pakštienė, E., Pawłowski, M., Pereira, W., Perig, V., Perła, J., Pilcher, F., Podlewska-Gaca, E., Polák, J., Polakis, T., Polińska, M., Popowicz, A., Richard, F., Rives, J. J., Rodrigues, T., Rogiński, Ł., Rondón, E., Rottenborn, M., Schäfer, R., Schnabel, C., Schreurs, O., Selva, A., Simon, M., Skiff, B., Skrutskie, M., Skrzypek, J., Sobkowiak, K., Sonbas, E., Sposetti, S., Stuart, P., Szyszka, K., Terakubo, K., Thomas, W., Trela, P., Uchiyama, S., Urbanik, M., Vaudescal, G., Venable, R., Watanabe, Ha., Watanabe, Hi., Winiarski, M., Wróblewski, R., Yamamura, H., Yamashita, M., Yoshihara, H., Zawilski, M., Zelený, P., Żejmo, M., Żukowski, K., Żywica, S.
Publikováno v:
A&A 679, A60 (2023)
As evidenced by recent survey results, majority of asteroids are slow rotators (P>12 h), but lack spin and shape models due to selection bias. This bias is skewing our overall understanding of the spins, shapes, and sizes of asteroids, as well as of
Externí odkaz:
http://arxiv.org/abs/2310.08995
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results i
Externí odkaz:
http://arxiv.org/abs/2310.02294