Analyzing Data Selection Techniques with Tools from the Theory of Information Losses

Autor: Foggo, Brandon, Yu, Nanpeng

In this paper, we present and illustrate some new tools for rigorously analyzing training data selection methods. These tools focus on the information theoretic losses that occur when sampling data. We use this framework to prove that two methods, Fa

Externí odkaz: http://arxiv.org/abs/1902.09602

Muon track reconstruction and data selection techniques in AMANDA

Autor: Ahrens, J., Bai, X., Bay, R., Barwick, S.W., Becka, T., Becker, J.K., Becker, K.-H., Bernardini, E., Bertrand, D., Biron, A., Boersma, D.J., Böser, S., Botner, O., Bouchta, A., Bouhali, O., Burgess, T., Carius, S., Castermans, T., Chirkin, D., Collin, B., Conrad, J., Cooley, J., Cowen, D.F., Davour, A., De Clercq, C., DeYoung, T., Desiati, P., Dewulf, J.-P., Ekström, P., Feser, T., Gaug, M., Gaisser, T.K., Ganugapati, R., Geenen, H., Gerhardt, L., Groß, A., Goldschmidt, A., Hallgren, A., Halzen, F., Hanson, K., Hardtke, R., Harenberg, T., Hauschildt, T., Helbing, K., Hellwig, M., Herquet, P., Hill, G.C., Hubert, D., Hughey, B., Hulth, P.O., Hultqvist, K., Hundertmark, S., Jacobsen, J., Karle, A., Kestel, M., Köpke, L., Kowalski, M., Kuehn, K., Lamoureux, J.I., Leich, H., Leuthold, M., Lindahl, P., Liubarsky, I., Madsen, J., Marciniewski, P., Matis, H.S., McParland, C.P., Messarius, T., Minaeva, Y., Miočinović, P., Mock, P.C., Morse, R., Münich, K.S., Nam, J., Nahnhauer, R., Neunhöffer, T., Niessen, P., Nygren, D.R., Ögelman, H., Olbrechts, Ph., Pérez de los Heros, C., Pohl, A.C., Porrata, R., Price, P.B., Przybylski, G.T., Rawlins, K., Resconi, E., Rhode, W., Ribordy, M., Richter, S., Rodrı́guez Martino, J., Ross, D., Sander, H.-G., Schinarakis, K., Schlenstedt, S., Schmidt, T., Schneider, D., Schwarz, R., Silvestri, A., Solarz, M., Spiczak, G.M., Spiering, C., Stamatikos, M., Steele, D., Steffen, P., Stokstad, R.G., Sulanke, K.-H., Streicher, O., Taboada, I., Thollander, L., Tilav, S., Wagner, W., Walck, C., Wang, Y.-R., Wiebusch, C.H. ^*, Wiedemann, C., Wischnewski, R., Wissing, H., Woschnagg, K., Yodh, G.

Publikováno v: In Nuclear Inst. and Methods in Physics Research, A 2004 524(1):169-194

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Comparison of Data Selection Techniques for the Translation of Video Lectures

Autor: Wuebker, Joern, Ney, Hermann, Martínez-Villaronga, Adrià, Giménez Pastor, Adrián, Juan Císcar, Alfonso, Servan, Christophe, Dymetman, Marc, Mirkin, Shashar

Publikováno v: The eleventh biennial conference of the Association for Machine Translation in the Americas (AMTA-2014)
The eleventh biennial conference of the Association for Machine Translation in the Americas (AMTA-2014), AMTA, Oct 2014, Vancouver, Canada
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname

[EN] For the task of online translation of scientific video lectures, using huge models is not possible. In order to get smaller and efficient models, we perform data selection. In this paper, we perform a qualitative and quantitative comparison of s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b40b33ab75f4c8cd7228cbbd5c6cd0a0
https://hal.archives-ouvertes.fr/hal-01157888/file/paperAMTA.pdf

An Empirical Analysis of Data Selection Techniques in Statistical Machine Translation

Autor: Chinea Ríos, Mara, Sanchis Trilles, Germán, Casacuberta Nolla, Francisco

Publikováno v: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)

[EN] Domain adaptation has recently gained interest in statistical machine translation. One of the adaptation techniques is based in the selection data. Data selection aims to select the best subset of the bilingual sentences from an available pool o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3ff0c85394b3002255bce1576ac10379
https://hdl.handle.net/10045/49281