
Explainability Requires Interactivity
When explaining the decisions of deep neural networks, simple stories ar...
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Explaining Bayesian Neural Networks
To make advanced learning machines such as Deep Neural Networks (DNNs) m...
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Finegrained Generalization Analysis of Structured Output Prediction
In machine learning we often encounter structured output prediction prob...
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Finegrained Generalization Analysis of Vectorvalued Learning
Many fundamental machine learning tasks can be formulated as a problem o...
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Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e.g. rotations, reflections, and cropping) play an...
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A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the...
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Input Hessian Regularization of Neural Networks
Regularizing the input gradient has shown to be effective in promoting t...
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Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
As a vital topic in media content interpretation, video anomaly detectio...
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Explainable Deep OneClass Classification
Deep oneclass classification variants for anomaly detection learn a map...
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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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Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem ...
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Orthogonal Inductive Matrix Completion
We propose orthogonal inductive matrix completion (OMIC), an interpretab...
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Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the nonideality of liquid...
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Simple and Effective Prevention of Mode Collapse in Deep OneClass Classification
Anomaly detection algorithms find extensive use in various fields. This ...
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Twosample Testing Using Deep Learning
We propose a twosample testing procedure based on learned deep neural n...
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Analyzing the Variance of Policy Gradient Estimators for the LinearQuadratic Regulator
We study the variance of the REINFORCE policy gradient estimator in envi...
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Deep SemiSupervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising resul...
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Improved Generalisation Bounds for Deep Learning Through L^∞ Covering Numbers
Using proof techniques involving L^∞ covering numbers, we show generalis...
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Scalable Generalized Dynamic Topic Models
Dynamic topic models (DTMs) model the evolution of prevalent themes in l...
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Efficient Gaussian Process Classification Using PolyaGamma Data Augmentation
We propose an efficient stochastic variational approach to GP classifica...
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Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector...
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Feature Importance Measure for Nonlinear Learning Algorithms
Complex problems may require sophisticated, nonlinear learning methods ...
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Framework for Multitask Multiple Kernel Learning and Applications in Genome Analysis
We present a general regularizationbased framework for Multitask learn...
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Probabilistic Clustering of TimeEvolving Distance Data
We present a novel probabilistic clustering model for objects that are r...
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Localized Complexities for Transductive Learning
We show two novel concentration inequalities for suprema of empirical pr...
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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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The Local Rademacher Complexity of LpNorm Multiple Kernel Learning
We derive an upper bound on the local Rademacher complexity of ℓ_pnorm ...
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Marius Kloft
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