tag :: explainable

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1The Power of Saying “I don’t know”—An Introduction to Subgroup Discovery and Local Modeling | realKD
Nice illustration why local models may make sense to pay attention to.
3 years ago by @becker
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discovery
explain
explainable
explanation
local
mining
pattern
subgroup
subgroups
uncertainty
example
illustration
discoveryexplainexplainableexplanationlocalminingpatternsubgroupsubgroupsuncertaintyexampleillustration
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4Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/
5 years ago by @becker
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interpretable
machine
learning
ml
xai
ai
artifical
intelligence
explainable
interpretablemachinelearningmlxaiaiartificalintelligenceexplainable
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2slundberg/shap: A unified approach to explain the output of any machine learning model.
https://github.com/slundberg/shap
6 years ago by @becker
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explainable
machine
learning
ml
explainability
interpretability
interpretation
neural
network
nn
code
implementation
shap
shaply
python
explainablemachinelearningmlexplainabilityinterpretabilityinterpretationneuralnetworknncodeimplementationshapshaplypython
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1How Artificial Intelligence Can Protect Financial Institutions From Malware Attacks
S. |. International Journal on Orange Technologies, 5 (5): 142-152 (May 2023)
a year ago by @researchpark_20
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AI
Artificial
Attacks,
Cybersecurity
Explainable
Financial
Institutions,
Intelligence,
Malware
Measures,
AIArtificialAttacks,CybersecurityExplainableFinancialInstitutions,Intelligence,MalwareMeasures,
(0)
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1Explainable AI for Games
J. Zhu, M. Awiszus, M. Cook, A. Dockhorn, M. Eberhardinger, D. Loiacono, S. Lucas, A. Matran-Fernandez, D. Liebana, T. Thompson and 1 other author(s). Human-Game AI Interaction (Dagstuhl Seminar 22251), 12 (6): 73-75 (January 2023)
a year ago by @tntl3s
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AI
Explainable
Games
for
leibnizailab
myown
AIExplainableGamesforleibnizailabmyown
(0)
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1Explainability in graph neural networks: A taxonomic survey
H. Yuan, H. Yu, S. Gui, and S. Ji. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2022)
2 years ago by @becker
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explainable
ai
xai
artificial
intelligence
graph
neural
networks
gnn
citedby:scholar:count:203
citedby:scholar:timestamp:2023-4-9
explainableaixaiartificialintelligencegraphneuralnetworksgnncitedby:scholar:count:203citedby:scholar:timestamp:2023-4-9
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2Explaining Data with Formal Concept Analysis
B. Ganter, S. Rudolph, and G. Stumme. page 153--195. Springer International Publishing, Cham, (2019)
2 years ago by @tomhanika
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explainable
fca
xai
explainablefcaxai
(0)
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1Explainable Deep Learning: A Field Guide for the Uninitiated
G. Ras, N. Xie, M. Gerven, and D. Doran. Journal of Artificial Intelligence Research, (January 2022)
2 years ago by @jaeschke
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deeplearning
explainable
learning
machine
ml
survey
deeplearningexplainablelearningmachinemlsurvey
(0)
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2Explanatory Interactive Machine Learning
S. Teso, and K. Kersting. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, ACM, (January 2019)
2 years ago by @jaeschke
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active
ai
artificial
explainable
intelligence
ki
learning
machine
ml
activeaiartificialexplainableintelligencekilearningmachineml
(0)
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1Explaining deep neural networks and beyond: A review of methods and applications
W. Samek, G. Montavon, S. Lapuschkin, C. Anders, and K. Müller. Proceedings of the IEEE, 109 (3): 247--278 (2021)
2 years ago by @becker
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explainable
machine
learning
neural
networks
themis
citedby:scholar:count:193
citedby:scholar:timestamp:2022-6-27
explainablemachinelearningneuralnetworksthemiscitedby:scholar:count:193citedby:scholar:timestamp:2022-6-27
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2Explainable Artificial Intelligence in education
H. Khosravi, S. Shum, G. Chen, C. Conati, D. Gasevic, J. Kay, S. Knight, R. Martinez-Maldonado, S. Sadiq, and Y. Tsai. Computers and Education: Artificial Intelligence, (2022)
3 years ago by @yish
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artificial
education
explainable
intelligence
artificialeducationexplainableintelligence
(0)
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1Utilizing XAI Technique to Improve Autoencoder based Model for Computer Network Anomaly Detection with Shapley Additive Explanation(SHAP)
K. Roshan, and A. Zafar. International Journal of Computer Networks & Communications (IJCNC), 13 (06): 109-128 (November 2021)
3 years ago by @laimbee
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(XAI)
AI
Additive
Anomaly
Autoencoder
Detection
Explainable
Explanation
Learning
Machine
Network
Security
Shapley
(XAI)AIAdditiveAnomalyAutoencoderDetectionExplainableExplanationLearningMachineNetworkSecurityShapley
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2Explainable Deep Few-shot Anomaly Detection with Deviation Networks
G. Pang, C. Ding, C. Shen, and A. van den Hengel. CoRR, (2021)
3 years ago by @andolab
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Anomaly
Deep
Detection
Deviation
Explainable
Few-shot
Networks
with
AnomalyDeepDetectionDeviationExplainableFew-shotNetworkswith
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3Explaining by removing: A unified framework for model explanation
I. Covert, S. Lundberg, and S. Lee. Journal of Machine Learning Research, 22 (209): 1--90 (2021)
3 years ago by @becker
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explaining
explainable
ai
artificial
intelligence
machine
learning
interpretable
feature
importance
citedby:scholar:count:13
citedby:scholar:timestamp:2021-11-23
explainingexplainableaiartificialintelligencemachinelearninginterpretablefeatureimportancecitedby:scholar:count:13citedby:scholar:timestamp:2021-11-23
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1A Survey on the Explainability of Supervised Machine Learning
N. Burkart, and M. Huber. Journal of Artificial Intelligence Research, (January 2021)
3 years ago by @tomhanika
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explainable
learning
supervised
xai
explainablelearningsupervisedxai
(0)
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2Explainable classification of brain networks via contrast subgraphs
T. Lanciano, F. Bonchi, and A. Gionis. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 3308--3318. (2020)
3 years ago by @becker
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kdd
contrast
subgraphs
graphs
correlation
disruption
networks
classification
explainable
interpretable
machine
learning
citedby:scholar:count:4
citedby:scholar:timestamp:2021-8-8
kddcontrastsubgraphsgraphscorrelationdisruptionnetworksclassificationexplainableinterpretablemachinelearningcitedby:scholar:count:4citedby:scholar:timestamp:2021-8-8
(0)
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1Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. sayres. Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 2668-2677. PMLR, (2018)
3 years ago by @jaeschke
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deeplearning
explainability
explainable
interpretability
interpretable
learning
machine
ml
model
deeplearningexplainabilityexplainableinterpretabilityinterpretablelearningmachinemlmodel
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4GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Q. Nguyen, K. Lim, D. Divakaran, K. Low, and M. Chan. Proc. IEEE Conf. Communications and Network Security (CNS), page 91--99. (June 2019)
4 years ago by @annakrause
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Anomaly
Detection,
Fingerprinting
GEE,
Gradient-based
Internet,
NetFlow
Records,
UGR
VAE,
analysis,
anomalous
anomaly
attack
autoencoder,
dataset,
deep-learning
detection
detection,
explainable
fingerprinting
gradient
gradient-based
learning
learning,
machine
methods,
models,
network
recording,
solutions,
statistical
technique,
techniques,
traffic
unsupervised
variational
zero-day
AnomalyDetection,FingerprintingGEE,Gradient-basedInternet,NetFlowRecords,UGRVAE,analysis,anomalousanomalyattackautoencoder,dataset,deep-learningdetectiondetection,explainablefingerprintinggradientgradient-basedlearninglearning,machinemethods,models,networkrecording,solutions,statisticaltechnique,techniques,trafficunsupervisedvariationalzero-day
(0)
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2Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
D. Afchar, and R. Hennequin. Fourteenth ACM Conference on Recommender Systems, ACM, (September 2020)
4 years ago by @nosebrain
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explainable
networks
neural
recommendation
explainablenetworksneuralrecommendation
(0)
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5Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
A. Ross, M. Hughes, and F. Doshi-Velez. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, page 2662--2670. (2017)
4 years ago by @schwemmlein
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ai
explainable
function
lime
loss
models
aiexplainablefunctionlimelossmodels
(0)
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1Explainable Agents and Robots: Results from a Systematic Literature Review
S. Anjomshoae, A. Najjar, D. Calvaresi, and K. Främling. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, page 1078–1088. Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems, (2019)
5 years ago by @porta
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XAI
agents
autonomous
explainable
goal-based
human
interaction
robot
XAIagentsautonomousexplainablegoal-basedhumaninteractionrobot
(0)
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1Interpreting tree ensembles with inTrees.
H. Deng. Int. J. Data Sci. Anal., 7 (4): 277-287 (2019)
5 years ago by @tomhanika
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ensembles
explainable
extraction
interpreting
learning
machine
rule
rules
xai
ensemblesexplainableextractioninterpretinglearningmachinerulerulesxai
(0)
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3Connectionist Expert Systems
S. Gallant. Communications of the ACM, (1988)
5 years ago by @tomhanika
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explainable
extraction
learning
machine
rules
xai
explainableextractionlearningmachinerulesxai
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1The Power of Saying “I don’t know”—An Introduction to Subgroup Discovery and Local Modeling | realKD

4Interpretable Machine Learning

2slundberg/shap: A unified approach to explain the output of any machine learning model.

publications (hide)28
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1How Artificial Intelligence Can Protect Financial Institutions From Malware Attacks

1Explainable AI for Games

1Explainability in graph neural networks: A taxonomic survey

2Explaining Data with Formal Concept Analysis

1Explainable Deep Learning: A Field Guide for the Uninitiated

2Explanatory Interactive Machine Learning

1Explaining deep neural networks and beyond: A review of methods and applications

2Explainable Artificial Intelligence in education

1Utilizing XAI Technique to Improve Autoencoder based Model for Computer Network Anomaly Detection with Shapley Additive Explanation(SHAP)

2Explainable Deep Few-shot Anomaly Detection with Deviation Networks

3Explaining by removing: A unified framework for model explanation

1A Survey on the Explainability of Supervised Machine Learning

2Explainable classification of brain networks via contrast subgraphs

1Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

4GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

2Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction

5Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

1Explainable Agents and Robots: Results from a Systematic Literature Review

1Interpreting tree ensembles with inTrees.

3Connectionist Expert Systems

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