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Journal articles on the topic 'Influence functions'

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Author: Grafiati
Published: 5 March 2024

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1

Pires, Ana M., and João A. Branco. "Partial Influence Functions." Journal of Multivariate Analysis 83, no. 2 (November 2002): 451–68. http://dx.doi.org/10.1006/jmva.2001.2055.

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2

Schioppa, Andrea, Polina Zablotskaia, David Vilar, and Artem Sokolov. "Scaling Up Influence Functions." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8179–86. http://dx.doi.org/10.1609/aaai.v36i8.20791.

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We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. With this improvement, we achieve, to the best of our knowledge, the first successful implementation of influence functions that scales to full-size (language and vision) Transformer models with several hundreds of millions of parameters. We evaluate our approach in image classification and sequence-to-sequence tasks with tens to a hundred of millions of training examples. Our code is available at https://github.com/google-research/jax-influence.
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3

Burke, Murray D., and Lajos Horváth. "Estimation of influence functions." Statistics & Probability Letters 4, no. 2 (March 1986): 81–85. http://dx.doi.org/10.1016/0167-7152(86)90022-2.

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4

Zdravkovic, Suncica. "Cognitive Functions Influence Lightness Perception." i-Perception 3, no. 9 (October 2012): 706. http://dx.doi.org/10.1068/if706.

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5

Ronchetti, Elvezio. "Robust inference by influence functions." Journal of Statistical Planning and Inference 57, no. 1 (January 1997): 59–72. http://dx.doi.org/10.1016/s0378-3758(96)00036-5.

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6

H Paynter, R. J., and D. Nowell. "Improved Influence Functions For Uniform Triangular Dislocation Density Functions." Journal of Strain Analysis for Engineering Design 40, no. 7 (October 1, 2005): 729–33. http://dx.doi.org/10.1243/030932405x30821.

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There is increasing interest in the use of dislocation methods for stress analysis. In a recent paper, McKellar, Hills, and Sackfield presented a method for obtaining the stress field induced by a triangular area of constant dislocation density. The current paper describes a simplification of the formulae, which is more robust and easier to implement. This new formulation easily extends to any polygon.
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7

Huang, Yufen, Tzu-Ling Kao, and Tai-Ho Wang. "Influence functions and local influence in linear discriminant analysis." Computational Statistics & Data Analysis 51, no. 8 (May 2007): 3844–61. http://dx.doi.org/10.1016/j.csda.2006.03.001.

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8

Huang, Yufen, Mei-Ling Kuo, and Tai-Ho Wang. "Pair-perturbation influence functions and local influence in PCA." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 5886–99. http://dx.doi.org/10.1016/j.csda.2006.11.005.

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9

Alhassan, Abukari. "Influence of Determinants of Diabetes on Sexual Quality using Discriminant Functions." Clinical Medical Reviews and Reports 3, no. 1 (February 20, 2021): 01–06. http://dx.doi.org/10.31579/2690-8794/063.

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This paper seeks to give a better understanding of the influence of age, creatinine level, duration of diabetes, glucose level and pulse rate on diabetic-induced sexual dysfunction among people. The study employed two-way MANOVA with balanced samples of 115 each. Prior to the analysis, univariate and multivariate normality, linearity, equality of error and covariance matrices, and multicollinearity assumptions were satisfied. Significance was attained for both sex and marital status (P < 0.05), though their interaction effect was not significant (P > 0.05). Wald’s test for individual groups confirmed the hypothetical decision of MANOVA, though independent T2 failed to report significance for marital status. Under the parameter estimation, it was ascertained that the difference between the groups was in terms of age and creatinine levels of people. The model is also considered credible since bootstrap estimates converged to the empirical estimates. Even though this paper did not exhaust all metabolic and comorbid factors relating to diabetes, it is hoped that it provides an insight into some circumstances in the evolution of diabetes.
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10

Gustafson, Paul. "Model influence functions based on mixtures." Canadian Journal of Statistics 24, no. 4 (December 1996): 535–48. http://dx.doi.org/10.2307/3315332.

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11

Avella-Medina, Marco. "Influence functions for penalized M-estimators." Bernoulli 23, no. 4B (November 2017): 3178–96. http://dx.doi.org/10.3150/16-bej841.

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12

Jorgensen, Murray A. "Influence Functions for Iteratively Defined Statistics." Biometrika 80, no. 2 (June 1993): 253. http://dx.doi.org/10.2307/2337197.

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13

Syta, Aneta, and Kinga Ziobrowska. "Influence of Ankyloglossia on Primary Functions." Logopedia Silesiana 8 (2019): 363–99. http://dx.doi.org/10.31261/logopediasilesiana.2019.08.16.

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14

Schuber, F. "Influence of polyamines on membrane functions." Biochemical Journal 260, no. 1 (May 15, 1989): 1–10. http://dx.doi.org/10.1042/bj2600001.

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15

Buck, Steven L., Roger Knight, Garth Fowler, and Brian Hunt. "Rod influence on hue-scaling functions." Vision Research 38, no. 21 (November 1998): 3259–63. http://dx.doi.org/10.1016/s0042-6989(97)00436-7.

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16

PRENDERGAST, L. A. "Influence Functions for Sliced Inverse Regression." Scandinavian Journal of Statistics 32, no. 3 (September 2005): 385–404. http://dx.doi.org/10.1111/j.1467-9469.2005.00447.x.

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17

REID, N., and H. CRÉPEAU. "Influence functions for proportional hazards regression." Biometrika 72, no. 1 (1985): 1–9. http://dx.doi.org/10.1093/biomet/72.1.1.

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18

Rayaprolu, DB, and DP Rooke. "INFLUENCE FUNCTIONS FOR INCLINED EDGE CRACKS." Fatigue & Fracture of Engineering Materials & Structures 21, no. 6 (June 1998): 761–69. http://dx.doi.org/10.1046/j.1460-2695.1998.00467.x.

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19

Menikoff, Arthur. "Actuator influence functions of active mirrors." Applied Optics 30, no. 7 (March 1, 1991): 833. http://dx.doi.org/10.1364/ao.30.000833.

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20

Grabisch, Michel, and Agnieszka Rusinowska. "Influence functions, followers and command games." Games and Economic Behavior 72, no. 1 (May 2011): 123–38. http://dx.doi.org/10.1016/j.geb.2010.06.003.

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21

Zhang, Shengyu, R. Douglas Martin, and Anthony A. Christidis. "Influence Functions for Risk and Performance Estimators." Journal of Mathematical Finance 11, no. 01 (2021): 15–47. http://dx.doi.org/10.4236/jmf.2021.111002.

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22

Yu, Wen, Ziqiang Zhao, and Ming Zheng. "Empirical likelihood methods based on influence functions." Statistics and Its Interface 5, no. 3 (2012): 355–66. http://dx.doi.org/10.4310/sii.2012.v5.n3.a8.

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23

Cohen, Matthew J., Irena F. Creed, Laurie Alexander, Nandita B. Basu, Aram J. K. Calhoun, Christopher Craft, Ellen D’Amico, et al. "Do geographically isolated wetlands influence landscape functions?" Proceedings of the National Academy of Sciences 113, no. 8 (February 8, 2016): 1978–86. http://dx.doi.org/10.1073/pnas.1512650113.

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Geographically isolated wetlands (GIWs), those surrounded by uplands, exchange materials, energy, and organisms with other elements in hydrological and habitat networks, contributing to landscape functions, such as flow generation, nutrient and sediment retention, and biodiversity support. GIWs constitute most of the wetlands in many North American landscapes, provide a disproportionately large fraction of wetland edges where many functions are enhanced, and form complexes with other water bodies to create spatial and temporal heterogeneity in the timing, flow paths, and magnitude of network connectivity. These attributes signal a critical role for GIWs in sustaining a portfolio of landscape functions, but legal protections remain weak despite preferential loss from many landscapes. GIWs lack persistent surface water connections, but this condition does not imply the absence of hydrological, biogeochemical, and biological exchanges with nearby and downstream waters. Although hydrological and biogeochemical connectivity is often episodic or slow (e.g., via groundwater), hydrologic continuity and limited evaporative solute enrichment suggest both flow generation and solute and sediment retention. Similarly, whereas biological connectivity usually requires overland dispersal, numerous organisms, including many rare or threatened species, use both GIWs and downstream waters at different times or life stages, suggesting that GIWs are critical elements of landscape habitat mosaics. Indeed, weaker hydrologic connectivity with downstream waters and constrained biological connectivity with other landscape elements are precisely what enhances some GIW functions and enables others. Based on analysis of wetland geography and synthesis of wetland functions, we argue that sustaining landscape functions requires conserving the entire continuum of wetland connectivity, including GIWs.
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24

Ruckdeschel, Peter, and Helmut Rieder. "Optimal influence curves for general loss functions." Statistics & Decisions 22, no. 3-2004 (March 2004): 201–23. http://dx.doi.org/10.1524/stnd.22.3.201.57067.

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25

Hayashi, Kuniyoshi. "Influence functions for a linear subspace method." Pattern Recognition 47, no. 6 (June 2014): 2241–54. http://dx.doi.org/10.1016/j.patcog.2013.11.030.

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26

Ganesamurthy, K., C. Hariharan, and S. Muruganantham. "Influence of temperature on nucleon structure functions." Nuclear Physics A 930 (October 2014): 46–56. http://dx.doi.org/10.1016/j.nuclphysa.2014.07.026.

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27

Hable, R., P. Ruckdeschel, and H. Rieder. "Optimal robust influence functions in semiparametric regression." Journal of Statistical Planning and Inference 140, no. 1 (January 2010): 226–45. http://dx.doi.org/10.1016/j.jspi.2009.07.010.

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28

van der Vaart, Aad. "Higher Order Tangent Spaces and Influence Functions." Statistical Science 29, no. 4 (November 2014): 679–86. http://dx.doi.org/10.1214/14-sts478.

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29

Köhn, F. M., I. Erdmann, T. Oeda, K. F. El Mulla, H. G. Schiefer, and W. B. Schill. "Influence of urogenital infections on sperm functions." Andrologia 30, S1 (April 27, 2009): 73–80. http://dx.doi.org/10.1111/j.1439-0272.1998.tb02829.x.

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30

PRABHALA, RAO H., LAIMA M. BRAUNE, HARINDER S. GAREWAL, and RONALD R. WATSON. "Influence of Beta-Carotene on Immune Functions." Annals of the New York Academy of Sciences 691, no. 1 Carotenoids i (December 1993): 262–63. http://dx.doi.org/10.1111/j.1749-6632.1993.tb26189.x.

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31

Shevela, K. Y., O. Y. Leplina, M. A. Tikhonova, and H. R. Chernykh. "B-CLL influence on T cell functions." European Journal of Cancer and Clinical Oncology 27 (January 1991): S56. http://dx.doi.org/10.1016/0277-5379(91)91396-z.

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32

Deryugin, Yuriy N., Maxim V. Vetchinnikov, and Dmitry A. Shishkanov. "Investigation of different influence functions in peridynamics." Zhurnal Srednevolzhskogo Matematicheskogo Obshchestva 25, no. 4 (December 30, 2023): 342–60. http://dx.doi.org/10.15507/2079-6900.25.202304.342-360.

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Peridynamics is a non–local numerical method for solving fracture problems based on integral equations. It is assumed that particles in a continuum are endowed with volume and interact with each other at a finite distance, as in molecular dynamics. The influence function in peridynamic models is used to limit the force acting on a particle and to adjust the bond strength depending on the distance between the particles. It satisfies certain continuity conditions and describes the behavior of non-local interaction. The article investigates various types of influence function in peridynamic models on the example of three-dimensional problems of elasticity and fracture. In the course of the work done, the bond-based and state-based fracture models used in the Sandia Laboratory are described, 6 types of influence functions for the bond-based model and 2 types of functions for the state-based model are presented, and the corresponding formulas for calculating the stiffness of the bond are obtained. For testing, we used the problem of propagation of a spherically symmetric elastic wave, which has an analytical solution, and a qualitative problem of destruction of a brittle disk under the action of a spherical impactor. Graphs of radial displacement are given, raster images of simulation results are shown.
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33

Wang, Jin, and Robert Serfling. "Influence functions for a general class of depth-based generalized quantile functions." Journal of Multivariate Analysis 97, no. 4 (April 2006): 810–26. http://dx.doi.org/10.1016/j.jmva.2005.07.002.

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34

Gasparovica, Madara, Irena Tuleiko, and Ludmila Aleksejeva. "Influence of Membership Functions on Classification of Multi-Dimensional Data." Scientific Journal of Riga Technical University. Computer Sciences 45, no. 1 (January 1, 2011): 78–84. http://dx.doi.org/10.2478/v10143-011-0046-x.

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Influence of Membership Functions on Classification of Multi-Dimensional Data The aim of this study is to explore whether the number of intervals for each attribute influences the classification result and whether a larger number of intervals provide better classification accuracy using the Fuzzy PRISM algorithm. The feature selection has been carried out using Fast correlation-based filter solution, and then the decreased data sets have been applied in experiments with preferences used in the previous experiment series. The article also provides conclusions about the obtained classification results and analyzes criteria of certain experiments and their impact on the final result. Also a series of experiments was carried out to assess how and whether the classification result is influenced by categorization of continuous data, which is one of the membership function construction steps; Fuzzy unordered rule induction algorithm was used. The experiments have been carried out using four real data sets - Golub leukemia, Singh prostate, as well as Gastric cancer and leukemia donor data sets of the Latvian Biomedical Research and Study Center.
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35

Mitra, I. N., Dr Sonia Kochhar, and Dr B. K. Agrawal. "Influence of Aging on Lung Functions in Elderly Male and Female Healthy Subjects." Asian Pacific Journal of Health Sciences 6, no. 1 (March 2019): 114–17. http://dx.doi.org/10.21276/apjhs.2019.6.1.17.

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36

Jung, Hong-Shik. "A Study on Informatization and Administrative Functions." Korean Journal of Policy Studies 5 (December 31, 1990): 129–41. http://dx.doi.org/10.52372/kjps05008.

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Influenced by the rapid development of information technology(IT) owing to the merger of computer and telecommunications technology, informatization has been fast advancing in the world. Informatization not only accelerates structural changes ranging from economics to society as a whole, but also has significant impacts upon national activities as well as individuals or society. In the light of implementing all the national activities in the future, therefore, developing trends, influence, and utilizing methods of the informatization are to be considered. Initiated by the governments, the major countries in the world have already made efforts to prepare for the plans to meet changes of the informatization. As a part of the plans, they are advancing to establish management systems of national administrative information handling and joint-utilization systems. These efforts are aimed at administrative efficiency, enabling to use fair, prompt and accurate information and ultimately, realizing 'a small and efficient government.
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37

Dennis Wijaya, Ermi Girsang, Sri Lestari Ramadhani, Sri Wahyuni Nasution, and Ulina Karo Karo. "Influence Of Organizing Functions, Direction Functions And Planning Functions On Nurse Performance At Hospital Royal Prima Medan." International Journal of Health and Pharmaceutical (IJHP) 2, no. 1 (February 6, 2022): 1–8. http://dx.doi.org/10.51601/ijhp.v2i1.21.

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The optimal implementation of the managerial function of the head of the room is one of the factors that affect the performance of the team leader and has an impact on the quality of nursing services. Phenomena that occur in hospitals are often the head of the room busy with meeting activities held by hospital management so that the implementation of the role and function of the head of the room as a clinical supervisor who becomes a consultant for team leaders in nursing care is often neglected.The results of the study using product moment analysis found that there was an effect of implementing the managerial function of the head of the room on the performance of the team leader (p = 0.00 r = 0.905). The results of multiple linear regression showed that only the implementation of the planning function influenced the team leader's performance (p = 0.039 B = 4.150). The conclusion is that the higher the implementation of the planning function carried out by the head of the room, the better the performance of the team leader. The implications for nursing in this research are that it is necessary to improve the ability of the planning function of the head of the room through strategic planning training and developing nursing managerial concepts.
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38

Dang, Xin, Robert Serfling, and Weihua Zhou. "Influence functions of some depth functions, and application to depth-weighted L-statistics." Journal of Nonparametric Statistics 21, no. 1 (January 2009): 49–66. http://dx.doi.org/10.1080/10485250802447981.

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39

Prendergast, Luke A., and Jodie A. Smith. "Influence functions for linear discriminant analysis: Sensitivity analysis and efficient influence diagnostics." Journal of Multivariate Analysis 190 (July 2022): 104993. http://dx.doi.org/10.1016/j.jmva.2022.104993.

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40

Gritsenko, S. A., E. M. Ermolova, A. N. Ruchai, S. M. Ermolov, and A. K. Bochkarev. "Influence of feed additives on sows’ reproductive functions." E3S Web of Conferences 254 (2021): 08012. http://dx.doi.org/10.1051/e3sconf/202125408012.

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One of the radical ways to prevent and reduce mycotoxicosis on the animal body is the use of adsorbing feed additives in the diet. Inclusion of complex mineral and vegetable complex additive Nabikat at 0,20% dose and mineral - dry content of Glaukonit substance of 0.25% in enceinte and nursing sows’ full-fledged combined fodder composition showed that they increase the digestibility of crude fiber in the uterus during deep pregnancy by 4.97 and 4.07%, as well as the digestibility of crude fat by 5.24 and 1.71% and deposit nitrogen in it. They amount to 2.54 g and 1.24 g respectively. Compared to Glaukonit, Nabikat has a more positive effect on the anabolic properties of metabolic body processes. As a result, sows’ diversity in this group was 17.3% higher than in the control group, reaching 0.4% in the lazurite group. The difference in piglets’ preservation amounted to 10.9% and 2.2%, as well as in the weight of the litter when weaning. The age amounted to 26.5 and 0.9, respectively.%. Nabikat addition to sows’ diet reduced feed costs per piglet by 19.1 -30.4%, using Glaukonit - by 4.7 -5.8%.
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41

Lipu�, Bogdan, and Nikola Guid. "Influence of Blending Functions on Volumetric Cloud Images." Journal of Computing and Information Technology 16, no. 1 (2008): 35. http://dx.doi.org/10.2498/cit.1000916.

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42

Law, John, F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel. "Robust Statistics-The Approach Based on Influence Functions." Statistician 35, no. 5 (1986): 565. http://dx.doi.org/10.2307/2987975.

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43

Beck, J. V., N. R. Keltner, and I. P. Schisler. "Influence functions for the unsteady surface element method." AIAA Journal 23, no. 12 (December 1985): 1978–82. http://dx.doi.org/10.2514/3.9205.

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44

Miao, Xiaoye, Yangyang Wu, Lu Chen, Yunjun Gao, Jun Wang, and Jianwei Yin. "Efficient and effective data imputation with influence functions." Proceedings of the VLDB Endowment 15, no. 3 (November 2021): 624–32. http://dx.doi.org/10.14778/3494124.3494143.

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Data imputation has been extensively explored to solve the missing data problem. The dramatically rising volume of missing data makes the training of imputation models computationally infeasible in real-life scenarios. In this paper, we propose an efficient and effective data imputation system with influence functions , named EDIT, which quickly trains a parametric imputation model with representative samples under imputation accuracy guarantees. EDIT mainly consists of two modules, i.e., an imputation influence evaluation (IIE) module and a representative sample selection (RSS) module. IIE leverages the influence functions to estimate the effect of (in)complete samples on the prediction result of parametric imputation models. RSS builds a minimum set of the high-effect samples to satisfy a user-specified imputation accuracy. Moreover, we introduce a weighted loss function that drives the parametric imputation model to pay more attention on the high-effect samples. Extensive experiments upon ten state-of-the-art imputation methods demonstrate that, EDIT adopts only about 5% samples to speed up the model training by 4x in average with more than 11% accuracy gain.
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45

Kõlvart, Karl Rene. "Influence of wolframin expression on dopamine receptor functions." Intrinsic Activity 5, Suppl. 2 (October 16, 2017): A2.8. http://dx.doi.org/10.25006/ia.5.s2-a2.8.

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46

Selvais, Charlotte, Stéphane Dedieu, William Hornebeck, and Hervé Emonard. "Post-translational proteolytic events influence LRP-1 functions." Bio-Medical Materials and Engineering 20, no. 3-4 (2010): 203–7. http://dx.doi.org/10.3233/bme-2010-0633.

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47

Nayak, B. Suresh, D. Venkatesh, and MK Yogesh. "Influence of Body Mass Index on Pulmonary Functions." International Journal of Physiology 1, no. 2 (2013): 144. http://dx.doi.org/10.5958/j.2320-608x.1.2.030.

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48

Iwasaki, Manabu. "Notes on the Influence Functions of Standardized Moments." Behaviormetrika 18, no. 29 (January 1991): 11–22. http://dx.doi.org/10.2333/bhmk.18.29_11.

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49

Li, Xiaomeng, Ying Chen, Naoki Kawazoe, and Guoping Chen. "Influence of microporous gelatin hydrogels on chondrocyte functions." Journal of Materials Chemistry B 5, no. 29 (2017): 5753–62. http://dx.doi.org/10.1039/c7tb01350g.

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Gelatin hydrogels with microporous structures were prepared by polymerization of GelMAGMA macromers and dissolution of gelatin hydrogel microcubes. The microporous gelatin hydrogels promoted proliferation of chondrocytes and showed different cellular effects compared to hydrogels without microporous structures.
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50

Dollinger, Michael B., and Robert G. Staudte. "Influence Functions of Iteratively Reweighted Least Squares Estimators." Journal of the American Statistical Association 86, no. 415 (September 1991): 709–16. http://dx.doi.org/10.1080/01621459.1991.10475099.

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