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List of papers in the area of Explainable Artificial Intelligence Year wise

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Explainable AI papers Year-wise

List of papers in the area of Explainable Artificial Intelligence Year wise

2014

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps pdf

Visualizing and understanding convolutional networks pdf

Object detectors emerge in deep scene CNN’s pdf

2015

Understanding Neural Network through Deep Visualization pdf

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation pdf

Understanding Deep Image Representation by Inverting Them pdf

Striving for Simplicity: The All Convolutional Net pdf

2016

An unexpected unity among methods for interpreting model predictions pdf

“Why Should I Trust you?” Explaining the Predictions of Any Classifier pdf

Learning deep features for discriminative localization pdf

2017

SmoothGrad: Removing Noise By Adding Noise pdf

Axiomatic Attribution for Deep Neural Networks pdf

Learning Important Features Through Propagating Activation Differences pdf

Understanding Black-box Predictions via Influence Functions pdf

Interpretable Explanations of Black Boxes by Meaningful Perturbation pdf

Visualizing deep neural network decisions: Prediction difference analysis pdf

Network Dissection: Quantifying Interpretability of Deep Visual Representations pdf

The (Un)reliability of Saliency Methods pdf

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations pdf

Understanding intermediate layers using linear classifier probes pdf

Using KL-divergence to focus Deep Visual Explanation pdf

A Unified Approach to Interpreting Model Predictions pdf

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization pdf

2018

Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions pdf

Towards Robust Interpretability with Self-Explaining Neural Networks pdf

Towards Better Understanding of Gradient-Based Attribution Methods For Deep Neural Networks pdf

Interpretable Convolutional Neural Networks pdf

Grad-cam++: Improved Visual Explanations for Deep Convolutional Networks pdf

Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in deep Neural Networks pdf

Sanity Checks for Saliency Maps pdf

Imagenet trained CNN’s are biased towards texture, increasing shape bias improves accuracy and robustness pdf

Anchors: High precision model agnostic explanations pdf

2019

A Simple Saliency Method That Passes the Sanity Check pdf

Bias Also Matters: Bias Attribution for Deep Neural Network Explanation pdf

Counterfactual Visual Explanations pdf

Explainable AI for Trees: From Local Explanations to Global Understanding pdf

Why did you do that?: Explaining black-box models with inductive synthesis pdf

TED: Teaching AI to explain its decisions pdf

This Looks Like That: Deep Learning for Interpretable Image Recognition pdf

Interpretable Image Recognition with Hierarchical Prototypes pdf

Understanding Deep Neural Networks For Regression In Leaf Counting pdf

Score-CAM: Improved Visual Explanations Via Score-Weighted Class Activation Mapping pdf

Black Box Explanation by Learning Image Exemplars in the Latent Feature Space pdf

Learning Reliable Visual Saliency for Model Explanations pdf

2020

Explanation by Progressive Exaggeration pdf

Workshops

https://xai.kdd2019.a.intuit.com/

https://human-centered.ai/methods-of-explainable-ai/

https://sites.google.com/view/xai2019/home

http://www.heatmapping.org/ ( contains a list of workshops )