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04/12/2023
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
Explainable AI has become a popular tool for validating machine learning...
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03/11/2023
Explainable AI for Time Series via Virtual Inspection Layers
The field of eXplainable Artificial Intelligence (XAI) has greatly advan...
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12/30/2022
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Explainable AI transforms opaque decision strategies of ML models into e...
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11/22/2022
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
While the evaluation of explanations is an important step towards trustw...
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02/15/2022
XAI for Transformers: Better Explanations through Conservative Propagation
Transformers have become an important workhorse of machine learning, wit...
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12/21/2021
Toward Explainable AI for Regression Models
In addition to the impressive predictive power of machine learning (ML) ...
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08/23/2021
Deep learning for surrogate modelling of 2D mantle convection
Traditionally, 1D models based on scaling laws have been used to paramet...
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06/09/2021
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Domain shifts in the training data are common in practical applications ...
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09/24/2020
A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the...
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08/12/2020
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
More and more diseases have been found to be strongly correlated with di...
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06/18/2020
The Clever Hans Effect in Anomaly Detection
The 'Clever Hans' effect occurs when the learned model produces correct ...
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06/05/2020
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
Graph Neural Networks (GNNs) are a popular approach for predicting graph...
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03/17/2020
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond
With the broader and highly successful usage of machine learning in indu...
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03/11/2020
Building and Interpreting Deep Similarity Models
Many learning algorithms such as kernel machines, nearest neighbors, clu...
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09/25/2019
Explaining and Interpreting LSTMs
While neural networks have acted as a strong unifying force in the desig...
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06/18/2019
From Clustering to Cluster Explanations via Neural Networks
A wealth of algorithms have been developed to extract natural cluster st...
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02/26/2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application prob...
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08/13/2018
iNNvestigate neural networks!
In recent years, deep neural networks have revolutionized many applicati...
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06/29/2018
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Detecting and explaining anomalies is a challenging effort. This holds e...
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06/11/2018
Understanding Patch-Based Learning by Explaining Predictions
Deep networks are able to learn highly predictive models of video data. ...
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05/16/2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
A common machine learning task is to discriminate between normal and ano...
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07/18/2017
Discovering topics in text datasets by visualizing relevant words
When dealing with large collections of documents, it is imperative to qu...
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07/17/2017
Exploring text datasets by visualizing relevant words
When working with a new dataset, it is important to first explore and fa...
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06/24/2017
Methods for Interpreting and Understanding Deep Neural Networks
This paper provides an entry point to the problem of interpreting a deep...
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06/22/2017
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Recently, a technique called Layer-wise Relevance Propagation (LRP) was ...
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12/23/2016
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Text documents can be described by a number of abstract concepts such as...
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06/23/2016
Explaining Predictions of Non-Linear Classifiers in NLP
Layer-wise relevance propagation (LRP) is a recently proposed technique ...
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06/23/2016
Identifying individual facial expressions by deconstructing a neural network
This paper focuses on the problem of explaining predictions of psycholog...
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04/04/2016
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Layer-wise relevance propagation is a framework which allows to decompos...
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12/08/2015
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Nonlinear methods such as Deep Neural Networks (DNNs) are the gold stand...
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12/01/2015
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular an...
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07/07/2015
Wasserstein Training of Boltzmann Machines
The Boltzmann machine provides a useful framework to learn highly comple...
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03/16/2012
