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EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep
  learning representations with expert knowledge graphs: the MonuMAI cultural
  heritage use case

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case

24 April 2021
Natalia Díaz Rodríguez
Alberto Lamas
Jules Sanchez
Gianni Franchi
Ivan Donadello
Siham Tabik
David Filliat
P. Cruz
Rosana Montes
Francisco Herrera
ArXivPDFHTML

Papers citing "EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case"

50 / 50 papers shown
Title
Classification by Attention: Scene Graph Classification with Prior
  Knowledge
Classification by Attention: Scene Graph Classification with Prior Knowledge
Sahand Sharifzadeh
Sina Moayed Baharlou
Volker Tresp
OCL
47
50
0
19 Nov 2020
Amortized Causal Discovery: Learning to Infer Causal Graphs from
  Time-Series Data
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Sindy Löwe
David Madras
R. Zemel
Max Welling
CML
BDL
AI4TS
79
131
0
18 Jun 2020
A Critic Evaluation of Methods for COVID-19 Automatic Detection from
  X-Ray Images
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
Gianluca Maguolo
L. Nanni
48
201
0
27 Apr 2020
Relational Neural Machines
Relational Neural Machines
G. Marra
Michelangelo Diligenti
Francesco Giannini
Marco Gori
Marco Maggini
NAI
BDL
69
38
0
06 Feb 2020
Analysis of Explainers of Black Box Deep Neural Networks for Computer
  Vision: A Survey
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
Vanessa Buhrmester
David Münch
Michael Arens
MLAU
FaML
XAI
AAML
55
361
0
27 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies,
  Opportunities and Challenges toward Responsible AI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
91
6,211
0
22 Oct 2019
Learning from both experts and data
Learning from both experts and data
R. Besson
E. L. Pennec
S. Allassonnière
32
4
0
20 Oct 2019
Learn to Explain Efficiently via Neural Logic Inductive Learning
Learn to Explain Efficiently via Neural Logic Inductive Learning
Yu’an Yang
Le Song
NAI
42
76
0
06 Oct 2019
Compensating Supervision Incompleteness with Prior Knowledge in Semantic
  Image Interpretation
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
Ivan Donadello
Luciano Serafini
28
25
0
01 Oct 2019
Semi-Supervised Learning using Differentiable Reasoning
Semi-Supervised Learning using Differentiable Reasoning
Emile van Krieken
Erman Acar
F. V. Harmelen
DRL
33
21
0
13 Aug 2019
Attention is not not Explanation
Attention is not not Explanation
Sarah Wiegreffe
Yuval Pinter
XAI
AAML
FAtt
48
901
0
13 Aug 2019
Neural Probabilistic Logic Programming in DeepProbLog
Neural Probabilistic Logic Programming in DeepProbLog
Robin Manhaeve
Sebastijan Dumancic
Angelika Kimmig
T. Demeester
Luc de Raedt
NAI
55
550
0
18 Jul 2019
Continual Learning for Robotics: Definition, Framework, Learning
  Strategies, Opportunities and Challenges
Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges
Timothée Lesort
Vincenzo Lomonaco
Andrei Stoian
Davide Maltoni
David Filliat
Natalia Díaz Rodríguez
CLL
56
249
0
29 Jun 2019
Neural-Symbolic Computing: An Effective Methodology for Principled
  Integration of Machine Learning and Reasoning
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
Artur Garcez
Marco Gori
Luís C. Lamb
Luciano Serafini
Michael Spranger
Son N. Tran
NAI
81
291
0
15 May 2019
ArCo: the Italian Cultural Heritage Knowledge Graph
ArCo: the Italian Cultural Heritage Knowledge Graph
Valentina Anita Carriero
Aldo Gangemi
Maria Letizia Mancinelli
Ludovica Marinucci
Andrea Giovanni Nuzzolese
Valentina Presutti
Chiara Veninata
25
102
0
07 May 2019
Neural Logic Machines
Neural Logic Machines
Honghua Dong
Jiayuan Mao
Tian Lin
Chong-Jun Wang
Lihong Li
Denny Zhou
NAI
LRM
AI4CE
102
249
0
26 Apr 2019
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and
  Sentences From Natural Supervision
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Jiayuan Mao
Chuang Gan
Pushmeet Kohli
J. Tenenbaum
Jiajun Wu
NAI
98
694
0
26 Apr 2019
Weakly Supervised Complementary Parts Models for Fine-Grained Image
  Classification from the Bottom Up
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
Weifeng Ge
Xiangru Lin
Yizhou Yu
56
256
0
07 Mar 2019
Attention is not Explanation
Attention is not Explanation
Sarthak Jain
Byron C. Wallace
FAtt
87
1,307
0
26 Feb 2019
Measuring Compositionality in Representation Learning
Measuring Compositionality in Representation Learning
Jacob Andreas
CoGe
55
146
0
19 Feb 2019
Efficient Concept Induction for Description Logics
Efficient Concept Induction for Description Logics
Md Kamruzzaman Sarker
Pascal Hitzler
31
38
0
08 Dec 2018
Sanity Checks for Saliency Maps
Sanity Checks for Saliency Maps
Julius Adebayo
Justin Gilmer
M. Muelly
Ian Goodfellow
Moritz Hardt
Been Kim
FAtt
AAML
XAI
115
1,947
0
08 Oct 2018
Adversarially Regularising Neural NLI Models to Integrate Logical
  Background Knowledge
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
Pasquale Minervini
Sebastian Riedel
AAML
NAI
GAN
40
119
0
26 Aug 2018
A Survey Of Methods For Explaining Black Box Models
A Survey Of Methods For Explaining Black Box Models
Riccardo Guidotti
A. Monreale
Salvatore Ruggieri
Franco Turini
D. Pedreschi
F. Giannotti
XAI
90
3,922
0
06 Feb 2018
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu
Zilu Zhang
Tal Friedman
Yitao Liang
Guy Van den Broeck
76
447
0
29 Nov 2017
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Tarek R. Besold
Artur Garcez
Sebastian Bader
Howard L. Bowman
Pedro M. Domingos
...
P. Lima
L. Penning
Gadi Pinkas
Hoifung Poon
Gerson Zaverucha
LRM
AI4CE
49
339
0
10 Nov 2017
The (Un)reliability of saliency methods
The (Un)reliability of saliency methods
Pieter-Jan Kindermans
Sara Hooker
Julius Adebayo
Maximilian Alber
Kristof T. Schütt
Sven Dähne
D. Erhan
Been Kim
FAtt
XAI
84
683
0
02 Nov 2017
Focal Loss for Dense Object Detection
Focal Loss for Dense Object Detection
Nayeon Lee
Priya Goyal
Ross B. Girshick
Kaiming He
Piotr Dollár
ObjD
105
2,993
0
07 Aug 2017
Convolutional 2D Knowledge Graph Embeddings
Convolutional 2D Knowledge Graph Embeddings
Tim Dettmers
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
GNN
3DV
163
2,591
0
05 Jul 2017
Teaching Compositionality to CNNs
Teaching Compositionality to CNNs
Austin Stone
Hua-Yan Wang
Michael Stark
Yi Liu
D. Phoenix
Dileep George
CoGe
40
54
0
14 Jun 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
484
129,831
0
12 Jun 2017
Logic Tensor Networks for Semantic Image Interpretation
Logic Tensor Networks for Semantic Image Interpretation
Ivan Donadello
Luciano Serafini
Artur Garcez
71
210
0
24 May 2017
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
574
21,613
0
22 May 2017
Learning how to explain neural networks: PatternNet and
  PatternAttribution
Learning how to explain neural networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans
Kristof T. Schütt
Maximilian Alber
K. Müller
D. Erhan
Been Kim
Sven Dähne
XAI
FAtt
59
338
0
16 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C. Fong
Andrea Vedaldi
FAtt
AAML
55
1,514
0
11 Apr 2017
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
125
3,848
0
10 Apr 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
127
5,920
0
04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
221
19,796
0
07 Oct 2016
Harnessing Deep Neural Networks with Logic Rules
Harnessing Deep Neural Networks with Logic Rules
Zhiting Hu
Xuezhe Ma
Zhengzhong Liu
Eduard H. Hovy
Eric Xing
AI4CE
NAI
39
613
0
21 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
598
16,828
0
16 Feb 2016
Learning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
SSL
SSeg
FAtt
174
9,280
0
14 Dec 2015
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.4K
192,638
0
10 Dec 2015
Explaining NonLinear Classification Decisions with Deep Taylor
  Decomposition
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
G. Montavon
Sebastian Lapuschkin
Alexander Binder
Wojciech Samek
Klaus-Robert Muller
FAtt
53
730
0
08 Dec 2015
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
  Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren
Kaiming He
Ross B. Girshick
Jian Sun
AIMat
ObjD
412
61,900
0
04 Jun 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual
  Attention
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Ke Xu
Jimmy Ba
Ryan Kiros
Kyunghyun Cho
Aaron Courville
Ruslan Salakhutdinov
R. Zemel
Yoshua Bengio
DiffM
286
10,034
0
10 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
889
149,474
0
22 Dec 2014
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
183
4,653
0
21 Dec 2014
Detect What You Can: Detecting and Representing Objects using Holistic
  Models and Body Parts
Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Xianjie Chen
Roozbeh Mottaghi
Xiaobai Liu
Sanja Fidler
R. Urtasun
Alan Yuille
77
639
0
08 Jun 2014
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in Context
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
283
43,290
0
01 May 2014
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
334
15,825
0
12 Nov 2013
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