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Academic literature on the topic 'Contrastive Explanat'
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Journal articles on the topic "Contrastive Explanat"
Jolayemi, Okanlawon Lekan, Jelili Titilola Opabode, and Gueye Badara. "In vitro response of three contrasting cassava (Manihot esculenta Crantz) varieties to mannitol-induced drought stress." Agricultura Tropica et Subtropica 51, no. 3 (September 1, 2018): 125–31. http://dx.doi.org/10.2478/ats-2018-0014.
Full textJolayemi, Okanlawon Lekan, Jelili Titilola Opabode, and Gueye Badara. "In vitro response of three contrasting cassava (Manihot esculenta Crantz) varieties to mannitol-induced drought stress." Agricultura Tropica et Subtropica 51, no. 3 (September 1, 2018): 125–31. http://dx.doi.org/10.1515/ats-2018-0014.
Full textStromberg, Zachary R., Rick E. Masonbrink, and Melha Mellata. "Transcriptomic Analysis of Shiga Toxin-Producing Escherichia coli during Initial Contact with Cattle Colonic Explants." Microorganisms 8, no. 11 (October 27, 2020): 1662. http://dx.doi.org/10.3390/microorganisms8111662.
Full textAbbasi, Bilal, Aisha Siddiquah, Duangjai Tungmunnithum, Shankhamala Bose, Muhammad Younas, Laurine Garros, Samantha Drouet, Nathalie Giglioli-Guivarc’h, and Christophe Hano. "Isodon rugosus (Wall. ex Benth.) Codd In Vitro Cultures: Establishment, Phytochemical Characterization and In Vitro Antioxidant and Anti-Aging Activities." International Journal of Molecular Sciences 20, no. 2 (January 21, 2019): 452. http://dx.doi.org/10.3390/ijms20020452.
Full textNiiniluoto, Ilkka. "Explanation by Idealized Theories." Kairos. Journal of Philosophy & Science 20, no. 1 (June 1, 2018): 43–63. http://dx.doi.org/10.2478/kjps-2018-0003.
Full textStarck, Zofia, and Barbara Witek-Czupryńska. "Diverse response of tomato fruit explants to high temperature." Acta Societatis Botanicorum Poloniae 62, no. 3-4 (2014): 165–69. http://dx.doi.org/10.5586/asbp.1993.025.
Full textDonovan, N. J., C. A. Offord, and J. L. Tyler. "Vegetative cutting and in vitro propagation of the tree waratah, Alloxylon flammeum P. Weston and Crisp (family Proteaceae)." Australian Journal of Experimental Agriculture 39, no. 2 (1999): 225. http://dx.doi.org/10.1071/ea97106.
Full textRehen, S. K., M. H. Varella, F. G. Freitas, M. O. Moraes, and R. Linden. "Contrasting effects of protein synthesis inhibition and of cyclic AMP on apoptosis in the developing retina." Development 122, no. 5 (May 1, 1996): 1439–48. http://dx.doi.org/10.1242/dev.122.5.1439.
Full textWeenink, Don, David van der Duin, Laura Keesman, Rozalie Lekkerkerk, Floris Mosselman, and Phie van Rompu. "Taking social ontology seriously: An interview with Jack Katz." Ethnography 21, no. 2 (February 25, 2020): 198–219. http://dx.doi.org/10.1177/1466138120907333.
Full textBelabbas, Hassiba, Santiago Zalvidea, Daniel Casellas, Jean-Pierre Molès, Olivier Galbes, Jacques Mercier, and Bernard Jover. "Contrasting effect of exercise and angiotensin II hypertension on in vivo and in vitro cardiac angiogenesis in rats." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 295, no. 5 (November 2008): R1512—R1518. http://dx.doi.org/10.1152/ajpregu.00014.2008.
Full textDissertations / Theses on the topic "Contrastive Explanat"
SEVESO, ANDREA. "Symbolic Reasoning for Contrastive Explanations." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404830.
Full textThe need for explanations of Machine Learning (ML) systems is growing as new models outperform their predecessors while becoming more complex and less comprehensible for their end-users. An essential step in eXplainable Artificial Intelligence (XAI) research is to create interpretable models that aim at approximating the decision function of a black box algorithm. Though several XAI methods have been proposed in recent years, not enough attention was paid to explaining how models change their behaviour in contrast with other versions (e.g., due to retraining or data shifts). In such cases, an XAI system should explain why the model changes its predictions concerning past outcomes. In several practical situations, human decision-makers deal with more than one machine learning model. Consequently, the importance of understanding how two machine learning models work beyond their prediction performances is growing, to understand their behavior, their differences, and their likeness. To date, interpretable models are synthesised for explaining black boxes and their predictions and can be beneficial for formally representing and measuring the differences in the retrained model's behaviour in dealing with new and different data. Capturing and understanding such differences is crucial, as the need for trust is key in any application to support human-Artificial Intelligence (AI) decision-making processes. This is the idea of ContrXT, a novel approach that (i) traces the decision criteria of a black box classifier by encoding the changes in the decision logic through Binary Decision Diagrams. Then (ii) it provides global, model-agnostic, Model-Contrastive (M-contrast) explanations in natural language, estimating why -and to what extent- the model has modified its behaviour over time. We implemented and evaluated this approach over several supervised ML models trained on benchmark datasets and a real-life application, showing it is effective in catching majorly changed classes and in explaining their variation through a user study. The approach has been implemented, and it is available to the community both as a python package and through REST API, providing contrastive explanations as a service.