
Explaining Bayesian Neural Networks
To make advanced learning machines such as Deep Neural Networks (DNNs) m...
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NoiseGrad: enhancing explanations by introducing stochasticity to model weights
Attribution methods remain a practical instrument that is used in realw...
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Optimal Sampling Density for Nonparametric Regression
We propose a novel active learning strategy for regression, which is mod...
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Langevin Cooling for Domain Translation
Domain translation is the task of finding correspondence between two dom...
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On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
In this work, we demonstrate that applying deep generative machine learn...
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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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Automatic Identification of Types of Alterations in Historical Manuscripts
Alterations in historical manuscripts such as letters represent a promis...
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WorstCase PolynomialTime Exact MAP Inference on Discrete Models with Global Dependencies
Considering the worstcase scenario, junction tree algorithm remains the...
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Asymptotically Unbiased Generative Neural Sampling
We propose a general framework for the estimation of observables with ge...
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Towards best practice in explaining neural network decisions with LRP
Within the last decade, neural network based predictors have demonstrate...
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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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Local Bandwidth Estimation via Mixture of Gaussian Processes
Real world data often exhibit inhomogeneity  complexity of the target f...
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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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Tight Bound of Incremental Cover Trees for Dynamic Diversification
Dynamic diversificationfinding a set of data points with maximum dive...
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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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Optimizing for Measure of Performance in MaxMargin Parsing
Many statistical learning problems in the area of natural language proce...
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Sharing Hash Codes for Multiple Purposes
Locality sensitive hashing (LSH) is a powerful tool for sublineartime a...
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SynsetRank: Degreeadjusted Random Walk for Relation Identification
In relation extraction, a key process is to obtain good detectors that f...
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Insights from Classifying Visual Concepts with Multiple Kernel Learning
Combining information from various image features has become a standard ...
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Shinichi Nakajima
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