Related references
Note: Only part of the references are listed.Improved clustering algorithms for image segmentation based on non-local information and back projection
Xiaofeng Zhang et al.
INFORMATION SCIENCES (2021)
A framework to identify structured behavioral patterns within rodent spatial trajectories
Francesco Donnarumma et al.
SCIENTIFIC REPORTS (2021)
A survey on modern trainable activation functions
Andrea Apicella et al.
NEURAL NETWORKS (2021)
Comparison of export and outward foreign direct investment models of Chinese enterprises based on quantitative algorithm
Danqi Li et al.
NEURAL COMPUTING & APPLICATIONS (2020)
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta et al.
INFORMATION FUSION (2020)
Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods
A. Apicella et al.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS (2020)
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges
David Charte et al.
NEUROCOMPUTING (2020)
Image segmentation using dense and sparse hierarchies of superpixels
Felipe Lemes Galvao et al.
PATTERN RECOGNITION (2020)
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller
ARTIFICIAL INTELLIGENCE (2019)
Disentangled Variational Auto-Encoder for semi-supervised learning
Yang Li et al.
INFORMATION SCIENCES (2019)
Definitions, methods, and applications in interpretable machine learning
W. James Murdoch et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
A hierarchical prototype-based approach for classification
Xiaowei Gu et al.
INFORMATION SCIENCES (2019)
Methods for interpreting and understanding deep neural networks
Gregoire Montavon et al.
DIGITAL SIGNAL PROCESSING (2018)
Unsupervised image segmentation via Stacked Denoising Auto-encoder and hierarchical patch indexing
Jun Yu et al.
SIGNAL PROCESSING (2018)
Visual interpretability for deep learning: a survey
Quan-shi Zhang et al.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING (2018)
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi et al.
IEEE ACCESS (2018)
Explaining nonlinear classification decisions with deep Taylor decomposition
Gregoire Montavon et al.
PATTERN RECOGNITION (2017)
Evaluating the Visualization of What a Deep Neural Network Has Learned
Wojciech Samek et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)
An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy
Hao Gao et al.
INFORMATION SCIENCES (2016)
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach et al.
PLOS ONE (2015)
Advertiser-centric approach to understand user click behavior in sponsored search
Sungchul Kim et al.
INFORMATION SCIENCES (2014)
Representation Learning: A Review and New Perspectives
Yoshua Bengio et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)
Scale-sets image analysis
Laurent Guigues et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2006)