
Fairwashing Explanations with OffManifold Detergent
Explanation methods promise to make blackbox classifiers more transpare...
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Explainable Deep OneClass Classification
Deep oneclass classification variants for anomaly detection learn a map...
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The Clever Hans Effect in Anomaly Detection
The 'Clever Hans' effect occurs when the learned model produces correct ...
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How Much Can I Trust You? – Quantifying Uncertainties in Explaining Neural Networks
Explainable AI (XAI) aims to provide interpretations for predictions mad...
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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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Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem ...
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Ensemble Learning of CoarseGrained Molecular Dynamics Force Fields with a Kernel Approach
Gradientdomain machine learning (GDML) is an accurate and efficient app...
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Risk Estimation of SARSCoV2 Transmission from Bluetooth Low Energy Measurements
Digital contact tracing approaches based on Bluetooth low energy (BLE) h...
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Automatic Identification of Types of Alterations in Historical Manuscripts
Alterations in historical manuscripts such as letters represent a promis...
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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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Building and Interpreting Deep Similarity Models
Many learning algorithms such as kernel machines, nearest neighbors, clu...
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Autonomous robotic nanofabrication with reinforcement learning
The ability to handle single molecules as effectively as macroscopic bui...
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Forecasting Industrial Aging Processes with Machine Learning Methods
By accurately predicting industrial aging processes (IAPs), it is possib...
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Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet unHans'ed
Today's machine learning models for computer vision are typically traine...
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Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
The success of convolutional neural networks (CNNs) in various applicati...
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Machine learning for molecular simulation
Machine learning (ML) is transforming all areas of science. The complex ...
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Clustered Federated Learning: ModelAgnostic Distributed MultiTask Optimization under Privacy Constraints
Federated Learning (FL) is currently the most widely adopted framework f...
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Towards Explainable Artificial Intelligence
In recent years, machine learning (ML) has become a key enabling technol...
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Explaining and Interpreting LSTMs
While neural networks have acted as a strong unifying force in the desig...
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Resolving challenges in deep learningbased analyses of histopathological images using explanation methods
Deep learning has recently gained popularity in digital pathology due to...
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Deep Transfer Learning For WholeBrain fMRI Analyses
The application of deep learning (DL) models to the decoding of cognitiv...
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Explanations can be manipulated and geometry is to blame
Explanation methods aim to make neural networks more trustworthy and int...
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From Clustering to Cluster Explanations via Neural Networks
A wealth of algorithms have been developed to extract natural cluster st...
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Deep SemiSupervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising resul...
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Evaluating Recurrent Neural Network Explanations
Over the last years machine learning (ML) has become a key enabling tech...
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BlackBox Decision based Adversarial Attack with Symmetric αstable Distribution
Developing techniques for adversarial attack and defense is an important...
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Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt  VANs Enhanced by Importance and MCMC Sampling
In this comment on "Solving Statistical Mechanics Using Variational Auto...
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Robust and CommunicationEfficient Federated Learning from NonIID Data
Federated Learning allows multiple parties to jointly train a deep learn...
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Local Bandwidth Estimation via Mixture of Gaussian Processes
Real world data often exhibit inhomogeneity  complexity of the target f...
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Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application prob...
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Automating the search for a patent's prior art with a full text similarity search
More than ever, technical inventions are the symbol of our society's adv...
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Learning representations of molecules and materials with atomistic neural networks
Deep Learning has been shown to learn efficient representations for stru...
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Interpretable LSTMs For WholeBrain Neuroimaging Analyses
The analysis of neuroimaging data poses several strong challenges, in pa...
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What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis
Machine learning (ML) techniques such as (deep) artificial neural networ...
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iNNvestigate neural networks!
In recent years, deep neural networks have revolutionized many applicati...
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Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
Interpretability of deep neural networks is a recently emerging area of ...
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Unsupervised Detection and Explanation of Latentclass Contextual Anomalies
Detecting and explaining anomalies is a challenging effort. This holds e...
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Quantumchemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications...
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Tight Bound of Incremental Cover Trees for Dynamic Diversification
Dynamic diversificationfinding a set of data points with maximum dive...
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Understanding PatchBased Learning by Explaining Predictions
Deep networks are able to learn highly predictive models of video data. ...
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Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder
Adversarial attacks on deep learning models have been demonstrated to be...
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Towards computational fluorescence microscopy: Machine learningbased integrated prediction of morphological and molecular tumor profiles
Recent advances in cancer research largely rely on new developments in m...
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Compact and Computationally Efficient Representation of Deep Neural Networks
Dot product operations between matrices are at the heart of almost any f...
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Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
Currently, progressively larger deep neural networks are trained on ever...
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Towards Explaining Anomalies: A Deep Taylor Decomposition of OneClass Models
A common machine learning task is to discriminate between normal and ano...
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Optimizing for Measure of Performance in MaxMargin Parsing
Many statistical learning problems in the area of natural language proce...
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Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
With the availability of large databases and recent improvements in deep...
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Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances ...
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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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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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