1
Relevance-driven Input Dropout: an Explanation-guided Regularization Technique
2
TF-LIME : Interpretation Method for Time-Series Models Based on Time–Frequency Features
3
Mechanistic understanding and validation of large AI models with SemanticLens
4
A Fresh Look at Sanity Checks for Saliency Maps
5
Toward Explainable Artificial Intelligence for Precision Pathology.
6
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation
7
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
8
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
9
Optimizing Explanations by Network Canonization and Hyperparameter Search
10
OpenXAI: Towards a Transparent Evaluation of Model Explanations
11
From attribution maps to human-understandable explanations through Concept Relevance Propagation
12
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
13
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
14
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations
15
Measurably Stronger Explanation Reliability Via Model Canonization
16
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective
17
ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
18
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
19
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
20
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
21
Revealing the unique features of each individual's muscle activation signatures
22
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
23
A Unifying Review of Deep and Shallow Anomaly Detection
24
Captum: A unified and generic model interpretability library for PyTorch
25
Array programming with NumPy
26
CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
27
Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation
28
Improved protein structure prediction using potentials from deep learning
29
Finding and removing Clever Hans: Using explanation methods to debug and improve deep models
30
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
31
Explaining Machine Learning Models for Clinical Gait Analysis
32
PyTorch: An Imperative Style, High-Performance Deep Learning Library
33
Towards Best Practice in Explaining Neural Network Decisions with LRP
34
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
35
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
36
Explaining and Interpreting LSTMs
37
InterpretML: A Unified Framework for Machine Learning Interpretability
38
BatchNorm Decomposition for Deep Neural Network Interpretation
39
Unmasking Clever Hans predictors and assessing what machines really learn
40
iNNvestigate neural networks!
41
Computationally Efficient Measures of Internal Neuron Importance
42
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
43
RISE: Randomized Input Sampling for Explanation of Black-box Models
44
How Important Is a Neuron?
45
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
46
A Survey of Methods for Explaining Black Box Models
47
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
48
SmoothGrad: removing noise by adding noise
49
A Unified Approach to Interpreting Model Predictions
50
Learning how to explain neural networks: PatternNet and PatternAttribution
51
Learning Important Features Through Propagating Activation Differences
52
Axiomatic Attribution for Deep Networks
53
Dermatologist-level classification of skin cancer with deep neural networks
54
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
55
Quantum-chemical insights from deep tensor neural networks
56
Top-Down Neural Attention by Excitation Backprop
58
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
59
Deep Residual Learning for Image Recognition
60
Explaining nonlinear classification decisions with deep Taylor decomposition
61
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
62
Evaluating the Visualization of What a Deep Neural Network Has Learned
63
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
64
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
65
Striving for Simplicity: The All Convolutional Net
66
Explaining prediction models and individual predictions with feature contributions
67
Very Deep Convolutional Networks for Large-Scale Image Recognition
68
ImageNet Large Scale Visual Recognition Challenge
69
Caffe: Convolutional Architecture for Fast Feature Embedding
70
Visualizing and Understanding Convolutional Networks
71
Scikit-learn: Machine Learning in Python
72
Torchvision the machine-vision package of torch
73
An Efficient Explanation of Individual Classifications using Game Theory
74
Polynomial calculation of the Shapley value based on sampling
75
The Need for Open Source Software in Machine Learning
76
A tutorial on spectral clustering
77
Image quality assessment: from error visibility to structural similarity
78
On Spectral Clustering: Analysis and an algorithm
79
Least squares quantization in PCM
80
The Rotation of Eigenvectors by a Perturbation. III
81
Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements
82
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
83
Clever Hans : the horse of Mr. Von Osten
84
From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation
85
Alibi Explain: Algorithms for Explaining Machine Learning Models
86
Explainable AI Methods - A Brief Overview
87
Layer-Wise Relevance Propagation: An Overview
88
Efficient learning machines: from kernel methods to deep learning
89
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
91
CuPy : A NumPy-Compatible Library for NVIDIA GPU Calculations
92
The LRP Toolbox for Artificial Neural Networks
93
AngularJS: A Modern MVC Framework in JavaScript
95
Web Development - Developing Web Applications with Python
97
Visualizing Data using t-SNE
98
Ground truth evaluation of neural network explanations with clevr-xai
99
A Random Walks View of Spectral Segmentation
100
HDF : THE HIERARCHICAL DATA FORMAT
102
This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning
104
The funders had no role in study design, data collection and analysis, decision to preparation of the manuscript. There was no additional external funding received for this study
105
“Investigating the relationship between debiasing and artifact removal using saliency maps,”
