Conformal Predictors in Semisupervised Case Dmitry Adamskiy, Ilia Nouretdinov, and Alexander Gammerman

2011 ◽  
pp. 59-68
Keyword(s):  
Conformal Predictors

MODELS BASED ON CONFORMAL PREDICTORS FOR DIAGNOSTIC SYSTEMS IN MEDICINE

2019 ◽  
Vol 2 (2) ◽  
pp. 127-137
Author(s):  
Igor Shevchuk ◽  
Nikolai Michaluk ◽  
Victoria Ruvinskaya
Keyword(s):  
Diagnostic Systems ◽  
Conformal Predictors

Accurate and reliable image classification by using conformal predictors in the TJ-II Thomson scattering

2010 ◽  
Vol 81 (10) ◽  
pp. 10E118 ◽  
Author(s):  
J. Vega ◽  
A. Murari ◽  
A. Pereira ◽  
S. González ◽  
I. Pastor
Keyword(s):  
Image Classification ◽  
Thomson Scattering ◽  
Conformal Predictors

scholarly journals Evolved decision trees as conformal predictors

2013 ◽  
Author(s):  
Ulf Johansson ◽  
Rikard Konig ◽  
Tuve Lofstrom ◽  
Henrik Bostrom
Keyword(s):  
Decision Trees ◽  
Conformal Predictors

scholarly journals Conformal predictors

2012 ◽  
Vol 1 (3) ◽  
pp. 203-204
Author(s):  
Alexander Gammerman
Keyword(s):  
Conformal Predictors

Uncertainty Quantification and Explainable Artificial Intelligence

2020 ◽  
Author(s):  
Maria Moreno de Castro
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Monte Carlo ◽  
Deep Learning ◽  
Uncertainty Quantification ◽  
Gaussian Processes ◽  
Model Complexity ◽  
Sensitivity Analyses ◽  
Conformal Predictors ◽  
Explainable Artificial Intelligence

<p>The presence of automated decision making continuously increases in today's society. Algorithms based in machine and deep learning decide how much we pay for insurance,  translate our thoughts to speech, and shape our consumption of goods (via e-marketing) and knowledge (via search engines). Machine and deep learning models are ubiquitous in science too, in particular, many promising examples are being developed to prove their feasibility for earth sciences applications, like finding temporal trends or spatial patterns in data or improving parameterization schemes for climate simulations. </p><p>However, most machine and deep learning applications aim to optimise performance metrics (for instance, accuracy, which stands for the times the model prediction was right), which are rarely good indicators of trust (i.e., why these predictions were right?). In fact, with the increase of data volume and model complexity, machine learning and deep learning  predictions can be very accurate but also prone to rely on spurious correlations, encode and magnify bias, and draw conclusions that do not incorporate the underlying dynamics governing the system. Because of that, the uncertainty of the predictions and our confidence in the model are difficult to estimate and the relation between inputs and outputs becomes hard to interpret. </p><p>Since it is challenging to shift a community from “black” to “glass” boxes, it is more useful to implement Explainable Artificial Intelligence (XAI) techniques right at the beginning of the machine learning and deep learning adoption rather than trying to fix fundamental problems later. The good news is that most of the popular XAI techniques basically are sensitivity analyses because they consist of a systematic perturbation of some model components in order to observe how it affects the model predictions. The techniques comprise random sampling, Monte-Carlo simulations, and ensemble runs, which are common methods in geosciences. Moreover, many XAI techniques are reusable because they are model-agnostic and must be applied after the model has been fitted. In addition, interpretability provides robust arguments when communicating machine and deep learning predictions to scientists and decision-makers.</p><p>In order to assist not only the practitioners but also the end-users in the evaluation of  machine and deep learning results, we will explain the intuition behind some popular techniques of XAI and aleatory and epistemic Uncertainty Quantification: (1) the Permutation Importance and Gaussian processes on the inputs (i.e., the perturbation of the model inputs), (2) the Monte-Carlo Dropout, Deep ensembles, Quantile Regression, and Gaussian processes on the weights (i.e, the perturbation of the model architecture), (3) the Conformal Predictors (useful to estimate the confidence interval on the outputs), and (4) the Layerwise Relevance Propagation (LRP), Shapley values, and Local Interpretable Model-Agnostic Explanations (LIME) (designed to visualize how each feature in the data affected a particular prediction). We will also introduce some best-practises, like the detection of anomalies in the training data before the training, the implementation of fallbacks when the prediction is not reliable, and physics-guided learning by including constraints in the loss function to avoid physical inconsistencies, like the violation of conservation laws. </p>

Speech neuromuscular decoding based on spectrogram images using conformal predictors with Bi-LSTM

2021 ◽  
Author(s):  
You Wang ◽  
Ming Zhang ◽  
Rumeng Wu ◽  
Hengyang Wang ◽  
Zhiyuan Luo ◽  
...  
Keyword(s):  
Conformal Predictors

scholarly journals Combination of inductive mondrian conformal predictors

2018 ◽  
Vol 108 (3) ◽  
pp. 489-510
Author(s):  
Paolo Toccaceli ◽  
Alexander Gammerman
Keyword(s):  
Conformal Predictors
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