| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A fusion spatial attention approach for few-shot learning
|
Song, Heda
|
|
|
81 |
C |
p. 187-202
|
artikel
|
| 2 |
A multi-representation re-ranking model for Personalized Product Search
|
Bassani, Elias
|
|
|
81 |
C |
p. 240-249
|
artikel
|
| 3 |
A novel model usability evaluation framework (MUsE) for explainable artificial intelligence
|
Dieber, Jürgen
|
|
|
81 |
C |
p. 143-153
|
artikel
|
| 4 |
CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
|
Arras, Leila
|
|
|
81 |
C |
p. 14-40
|
artikel
|
| 5 |
ContrXT: Generating contrastive explanations from any text classifier
|
Malandri, Lorenzo
|
|
|
81 |
C |
p. 103-115
|
artikel
|
| 6 |
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
|
Chou, Yu-Liang
|
|
|
81 |
C |
p. 59-83
|
artikel
|
| 7 |
Deriving the personalized individual semantics of linguistic information from flexible linguistic preference relations
|
Jiang, Le
|
|
|
81 |
C |
p. 154-170
|
artikel
|
| 8 |
Editorial Board
|
|
|
|
81 |
C |
p. ii-iii
|
artikel
|
| 9 |
Explaining the impact of source behaviour in evidential reasoning
|
Kowalski, Paweł
|
|
|
81 |
C |
p. 41-58
|
artikel
|
| 10 |
Identifying user geolocation with Hierarchical Graph Neural Networks and explainable fusion
|
Zhou, Fan
|
|
|
81 |
C |
p. 1-13
|
artikel
|
| 11 |
Information fusion for edge intelligence: A survey
|
Zhang, Yin
|
|
|
81 |
C |
p. 171-186
|
artikel
|
| 12 |
Intrinsic Cramér–Rao bounds for distributed Bayesian estimator
|
Tnunay, Hilton
|
|
|
81 |
C |
p. 129-142
|
artikel
|
| 13 |
Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI)
|
Rožanec, Jože M.
|
|
|
81 |
C |
p. 91-102
|
artikel
|
| 14 |
Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions
|
Rahate, Anil
|
|
|
81 |
C |
p. 203-239
|
artikel
|
| 15 |
Neural generators of sparse local linear models for achieving both accuracy and interpretability
|
Yoshikawa, Yuya
|
|
|
81 |
C |
p. 116-128
|
artikel
|
| 16 |
Tabular data: Deep learning is not all you need
|
Shwartz-Ziv, Ravid
|
|
|
81 |
C |
p. 84-90
|
artikel
|
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