13.6.3.2 Explainable Aritficial Intelligence

Chapter Contents (Back)
Explainable. Knowledge. Applied to CNNs especially:
See also Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks.

Wellman, M.P., Henrion, M.,
Explaining 'explaining away',
PAMI(15), No. 3, March 1993, pp. 287-292.
IEEE DOI 0401
BibRef

Montavon, G.[Grégoire], Lapuschkin, S.[Sebastian], Binder, A.[Alexander], Samek, W.[Wojciech], Müller, K.R.[Klaus-Robert],
Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition,
PR(65), No. 1, 2017, pp. 211-222.
Elsevier DOI 1702
Award, Pattern Recognition, Best Paper. Deep neural networks BibRef

Lapuschkin, S., Binder, A., Montavon, G.[Grégoire], Müller, K.R.[Klaus-Robert], Samek, W.[Wojciech],
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,
CVPR16(2912-2920)
IEEE DOI 1612
BibRef

Jung, A., Nardelli, P.H.J.,
An Information-Theoretic Approach to Personalized Explainable Machine Learning,
SPLetters(27), 2020, pp. 825-829.
IEEE DOI 2006
Predictive models, Data models, Probabilistic logic, Machine learning, Decision making, Linear regression, decision support systems BibRef

Muñoz-Romero, S.[Sergio], Gorostiaga, A.[Arantza], Soguero-Ruiz, C.[Cristina], Mora-Jiménez, I.[Inmaculada], Rojo-Álvarez, J.L.[José Luis],
Informative variable identifier: Expanding interpretability in feature selection,
PR(98), 2020, pp. 107077.
Elsevier DOI 1911
Feature selection, Interpretability, Explainable machine learning, Resampling, Classification BibRef

Kauffmann, J.[Jacob], Müller, K.R.[Klaus-Robert], Montavon, G.[Grégoire],
Towards explaining anomalies: A deep Taylor decomposition of one-class models,
PR(101), 2020, pp. 107198.
Elsevier DOI 2003
Outlier detection, Explainable machine learning, Deep Taylor decomposition, Kernel machines, Unsupervised learning BibRef

Luo, J.[Jie], Zhao, J.[Jia], Wen, B.[Bin], Zhang, Y.H.[Yu-Hang],
Explaining the semantics capturing capability of scene graph generation models,
PR(110), 2021, pp. 107427.
Elsevier DOI 2011
Explanation, Metrics, Semantic property, Scene graph generation, Deep neural network BibRef

Yeom, S.K.[Seul-Ki], Seegerer, P.[Philipp], Lapuschkin, S.[Sebastian], Binder, A.[Alexander], Wiedemann, S.[Simon], Müller, K.R.[Klaus-Robert], Samek, W.[Wojciech],
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning,
PR(115), 2021, pp. 107899.
Elsevier DOI 2104
Pruning, Layer-wise relevance propagation (LRP), Convolutional neural network (CNN), Interpretation of models, Explainable AI (XAI) BibRef

Pierrard, R.[Régis], Poli, J.P.[Jean-Philippe], Hudelot, C.[Céline],
Spatial relation learning for explainable image classification and annotation in critical applications,
AI(292), 2021, pp. 103434.
Elsevier DOI 2102
Explainable artificial intelligence, Relation learning, Fuzzy logic BibRef

Langer, M.[Markus], Oster, D.[Daniel], Speith, T.[Timo], Hermanns, H.[Holger], Kästner, L.[Lena], Schmidt, E.[Eva], Sesing, A.[Andreas], Baum, K.[Kevin],
What do we want from Explainable Artificial Intelligence (XAI)?: A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research,
AI(296), 2021, pp. 103473.
Elsevier DOI 2106
Explainable Artificial Intelligence, Explainability, Interpretability, Explanations, Understanding, Human-Computer Interaction BibRef


Ortega, A.[Alfonso], Fierrez, J.[Julian], Morales, A.[Aythami], Wang, Z.L.[Zi-Long], Ribeiro, T.[Tony],
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment,
WACVW21(78-87) Explainable and Interpretable AI
IEEE DOI 2105
Training, Machine learning algorithms, Biometrics (access control), Resumes, Neural networks, Tools BibRef

Kwon, H.J.[Hyuk Jin], Koo, H.I.[Hyung Il], Cho, N.I.[Nam Ik],
Improving Explainability of Integrated Gradients with Guided Non-Linearity,
ICPR21(385-391)
IEEE DOI 2105
Measurement, Heating systems, Visualization, Gradient methods, Action potentials, Perturbation methods, Neurons BibRef

Fuhl, W.[Wolfgang], Rong, Y.[Yao], Motz, T.[Thomas], Scheidt, M.[Michael], Hartel, A.[Andreas], Koch, A.[Andreas], Kasneci, E.[Enkelejda],
Explainable Online Validation of Machine Learning Models for Practical Applications,
ICPR21(3304-3311)
IEEE DOI 2105
Machine learning algorithms, Microcontrollers, Memory management, Data acquisition, Training data, Transforms, Machine learning BibRef

Mänttäri, J.[Joonatan], Broomé, S.[Sofia], Folkesson, J.[John], Kjellström, H.[Hedvig],
Interpreting Video Features: A Comparison of 3d Convolutional Networks and Convolutional LSTM Networks,
ACCV20(V:411-426).
Springer DOI 2103

See also Interpretable Explanations of Black Boxes by Meaningful Perturbation. BibRef

Oussalah, M.[Mourad],
Ai Explainability. A Bridge Between Machine Vision and Natural Language Processing,
EDL-AI20(257-273).
Springer DOI 2103
BibRef

Petkovic, D., Alavi, A., Cai, D., Wong, M.,
Random Forest Model and Sample Explainer for Non-experts in Machine Learning: Two Case Studies,
EDL-AI20(62-75).
Springer DOI 2103
BibRef

Muddamsetty, S.M.[Satya M.], Jahromi, M.N.S.[Mohammad N. S.], Moeslund, T.B.[Thomas B.],
Expert Level Evaluations for Explainable Ai (XAI) Methods in the Medical Domain,
EDL-AI20(35-46).
Springer DOI 2103
BibRef

Muddamsetty, S.M., Mohammad, N.S.J., Moeslund, T.B.,
SIDU: Similarity Difference And Uniqueness Method for Explainable AI,
ICIP20(3269-3273)
IEEE DOI 2011
Visualization, Predictive models, Machine learning, Computational modeling, Measurement, Task analysis, Explainable AI, CNN BibRef

Sun, Y.C.[You-Cheng], Chockler, H.[Hana], Huang, X.W.[Xiao-Wei], Kroening, D.[Daniel],
Explaining Image Classifiers Using Statistical Fault Localization,
ECCV20(XXVIII:391-406).
Springer DOI 2011
BibRef

Choi, H., Som, A., Turaga, P.,
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification,
Diff-CVML20(3659-3666)
IEEE DOI 2008
Training, Task analysis, Feature extraction, Euclidean distance, Airplanes, Computer vision, Media BibRef

Parafita, Á., Vitrià, J.,
Explaining Visual Models by Causal Attribution,
VXAI19(4167-4175)
IEEE DOI 2004
data handling, feature extraction, intervened causal model, causal attribution, visual models, image generative models, learning BibRef

Schlegel, U., Arnout, H., El-Assady, M., Oelke, D., Keim, D.A.,
Towards A Rigorous Evaluation Of XAI Methods On Time Series,
VXAI19(4197-4201)
IEEE DOI 2004
image processing, learning (artificial intelligence), text analysis, time series, SHAP, image domain, text-domain, explainable-ai-evaluation BibRef

Fong, R.C.[Ruth C.], Vedaldi, A.[Andrea],
Interpretable Explanations of Black Boxes by Meaningful Perturbation,
ICCV17(3449-3457)
IEEE DOI 1802
Explain the result of learning. image classification, learning (artificial intelligence), black box algorithm, black boxes, classifier decision, Visualization BibRef

Hossam, M.[Mahmoud], Le, T.[Trung], Zhao, H.[He], Phung, D.[Dinh],
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability,
ICPR21(8922-8928)
IEEE DOI 2105
Training, Deep learning, Computational modeling, Perturbation methods, Text categorization, Natural languages, Training data BibRef

Plummer, B.A.[Bryan A.], Vasileva, M.I.[Mariya I.], Petsiuk, V.[Vitali], Saenko, K.[Kate], Forsyth, D.A.[David A.],
Why Do These Match? Explaining the Behavior of Image Similarity Models,
ECCV20(XI:652-669).
Springer DOI 2011
BibRef

Cheng, X., Rao, Z., Chen, Y., Zhang, Q.,
Explaining Knowledge Distillation by Quantifying the Knowledge,
CVPR20(12922-12932)
IEEE DOI 2008
Visualization, Task analysis, Measurement, Knowledge engineering, Optimization, Entropy, Neural networks BibRef

Chen, Y.,
Nonparametric Learning Via Successive Subspace Modeling (SSM),
ICIP19(3031-3032)
IEEE DOI 1910
Machine Learning, Explainable Machine Learning, Nonparametric Learning, Subspace Modeling, Successive Subspace Modeling BibRef

Shi, J.X.[Jia-Xin], Zhang, H.W.[Han-Wang], Li, J.Z.[Juan-Zi],
Explainable and Explicit Visual Reasoning Over Scene Graphs,
CVPR19(8368-8376).
IEEE DOI 2002
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Constraint Based Matching .


Last update:Jul 11, 2021 at 20:18:24