[HTML][HTML] Notions of explainability and evaluation approaches for explainable artificial intelligence
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …
the last few years. This is due to the widespread application of machine learning, particularly …
Explainability in graph neural networks: A taxonomic survey
Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …
intelligence tasks. A major limitation of deep models is that they are not amenable to …
Explainable AI: the new 42?
Explainable AI is not a new field. Since at least the early exploitation of CS Pierce's
abductive reasoning in expert systems of the 1980s, there were reasoning architectures to …
abductive reasoning in expert systems of the 1980s, there were reasoning architectures to …
From machine learning to explainable AI
A Holzinger - 2018 world symposium on digital intelligence for …, 2018 - ieeexplore.ieee.org
The success of statistical machine learning (ML) methods made the field of Artificial
Intelligence (AI) so popular again, after the last AI winter. Meanwhile deep learning …
Intelligence (AI) so popular again, after the last AI winter. Meanwhile deep learning …
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage …
Abstract The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning algorithms …
achieved an unprecedented performance over classical machine learning algorithms …
Predicting total drug clearance and volumes of distribution using the machine learning-mediated multimodal method through the imputation of various nonclinical data
H Iwata, T Matsuo, H Mamada… - Journal of Chemical …, 2022 - ACS Publications
Pharmacokinetic research plays an important role in the development of new drugs.
Accurate predictions of human pharmacokinetic parameters are essential for the success of …
Accurate predictions of human pharmacokinetic parameters are essential for the success of …
A generalized graph regularized non-negative tucker decomposition framework for tensor data representation
Non-negative Tucker decomposition (NTD) is one of the most popular techniques for tensor
data representation. To enhance the representation ability of NTD by multiple intrinsic cues …
data representation. To enhance the representation ability of NTD by multiple intrinsic cues …
Unsupervised deep tensor network for hyperspectral–multispectral image fusion
Fusing low-resolution (LR) hyperspectral images (HSIs) with high-resolution (HR)
multispectral images (MSIs) is a significant technology to enhance the resolution of HSIs …
multispectral images (MSIs) is a significant technology to enhance the resolution of HSIs …
Basic issues and challenges on Explainable Artificial Intelligence (XAI) in healthcare systems
OI Dauda, JB Awotunde, M AbdulRaheem… - … and methods of …, 2022 - igi-global.com
Artificial intelligence (AI) studies are progressing at a breakneck pace, with prospective
programs in healthcare industries being established. In healthcare, there has been an …
programs in healthcare industries being established. In healthcare, there has been an …
Automated data augmentations for graph classification
Data augmentations are effective in improving the invariance of learning machines. We
argue that the core challenge of data augmentations lies in designing data transformations …
argue that the core challenge of data augmentations lies in designing data transformations …