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Relevant bibliographies by topics / Influence functions

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Journal articles

Academic literature on the topic 'Influence functions'

Author: Grafiati

Published: 5 March 2024

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Journal articles on the topic "Influence functions"

1

Pires, Ana M., and João A. Branco. "Partial Influence Functions." Journal of Multivariate Analysis 83, no. 2 (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 (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 (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 (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 (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 (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 (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 (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 (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 (1996): 535–48. http://dx.doi.org/10.2307/3315332.

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