AIMC Topic: Bias

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RDE: A novel approach to improve the classification performance and expressivity of KDB.

PloS one Jul 23, 2018
Bayesian network classifiers (BNCs) have demonstrated competitive classification performance in a variety of real-world applications. A highly scalable BNC with high expressivity is extremely desirable. This paper proposes Redundant Dependence Elimin...
Machine Learning Bias Bayes Theorem Software Data Mining Classification
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On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines.

Neural networks : the official journal of the International Neural Network Society Jun 22, 2018
In this paper, we analyze the role of hidden bias in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines (GBPRBMs), which are similar to the widely used Gaussian-Bernoulli RBMs. Our experiments show that hidden bias play...
Entropy Neural Networks, Computer Likelihood Functions Bias Machine Learning Normal Distribution
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Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

Statistics in medicine Jun 4, 2018
Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric...
Machine Learning Models, Statistical Regression Analysis Humans Confidence Intervals Random Allocation Statistics as Topic Data Interpretation, Statistical Bias
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Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.

Statistical methods in medical research May 2, 2018
Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produ...
Humans Machine Learning Causality Probability Models, Statistical Propensity Score Bias
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Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.

Neural networks : the official journal of the International Neural Network Society Apr 9, 2018
Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has s...
Supervised Machine Learning Neural Networks, Computer Bias
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Too Much of a Good Thing: How Novelty Biases and Vocabulary Influence Known and Novel Referent Selection in 18-Month-Old Children and Associative Learning Models.

Cognitive science Apr 6, 2018
Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word-referent link. Prior work i...
Vocabulary Verbal Learning Knowledge Bases Attention Language Development Recognition, Psychology Computer Simulation Female Infant Bias Humans Male
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Response to Dr. Livia Puljak's letter about our published article.

Journal of clinical epidemiology Mar 12, 2018
Machine Learning Bias Prospective Studies Cross-Sectional Studies
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Technology-assisted risk of bias assessment in systematic reviews requires precise definitions of risk of bias.

Journal of clinical epidemiology Mar 10, 2018
Machine Learning Cross-Sectional Studies Bias Prospective Studies
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Technology-assisted risk of bias assessment in systematic reviews: a prospective cross-sectional evaluation of the RobotReviewer machine learning tool.

Journal of clinical epidemiology Dec 28, 2017
OBJECTIVES: To evaluate the reliability of RobotReviewer's risk of bias judgments.
Reproducibility of Results Systematic Reviews as Topic Cross-Sectional Studies Technology Humans Bias Prospective Studies Machine Learning
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The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity.

Science and engineering ethics Sep 21, 2017
Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines deliver...
Sexism Prejudice Racism Learning Datasets as Topic Female Biomedical Technology Humans Artificial Intelligence Automobiles Male Robotics Algorithms Social Justice Bias
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