
Towards Explainable Artificial Intelligence
In recent years, machine learning (ML) has become a key enabling technol...
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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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Shearlets as Feature Extractor for Semantic Edge Detection: The ModelBased and DataDriven Realm
Semantic edge detection has recently gained a lot of attention as an ima...
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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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Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it...
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Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
Variational inference has become one of the most widely used methods in ...
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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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A Markerless Deep Learningbased 6 Degrees of Freedom PoseEstimation for with Mobile Robots using RGB Data
Augmented Reality has been subject to various integration efforts within...
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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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Robust and CommunicationEfficient Federated Learning from NonIID Data
Federated Learning allows multiple parties to jointly train a deep learn...
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Deep SemiSupervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising resul...
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Videobased Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes
Avoiding bottleneck situations in crowds is critical for the safety and ...
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A 3DDeepLearningbased Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data
Augmented Reality and mobile robots are gaining much attention within in...
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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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A deep neural network for molecular wave functions in quasiatomic minimal basis representation
The emergence of machine learning methods in quantum chemistry provides ...
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Autoencoderbased Condition Monitoring and Anomaly Detection Method for Rotating Machines
Rotating machines like engines, pumps, or turbines are ubiquitous in mod...
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Learning The Invisible: A Hybrid Deep LearningShearlet Framework for Limited Angle Computed Tomography
The high complexity of various inverse problems poses a significant chal...
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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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Local Bandwidth Estimation via Mixture of Gaussian Processes
Real world data often exhibit inhomogeneity  complexity of the target f...
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MultiClass Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multiclass Gaussian process classification ap...
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Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision
The interpretation of data is fundamental to machine learning. This pape...
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Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
We present an approximate Bayesian inference approach for estimating the...
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Backprop Evolution
The backpropagation algorithm is the cornerstone of deep learning. Desp...
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Anomaly Detection with HMM Gauge Likelihood Analysis
This paper describes a new method, HMM gauge likelihood analysis, or GLA...
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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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VRLS: A Unified Reinforcement Learning Scheduler for VehicletoVehicle Communications
Vehicletovehicle (V2V) communications have distinct challenges that ne...
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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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Molecular Dynamics with NeuralNetwork Potentials
Molecular dynamics simulations are an important tool for describing the ...
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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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Generating equilibrium molecules with deep neural networks
Discovery of atomistic systems with desirable properties is a major chal...
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Differential Privacy for Eye Tracking with Temporal Correlations
Head mounted displays bring eye tracking into daily use and this raises ...
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Controlling Fairness and Bias in Dynamic LearningtoRank
Rankings are the primary interface through which many online platforms m...
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On the minimum FLOPs problem in the sparse Cholesky factorization
Prior to computing the Cholesky factorization of a sparse, symmetric pos...
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Computational Complexity Aspects of Point Visibility Graphs
A point visibility graph is a graph induced by a set of points in the pl...
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Finding Shortest Paths between Graph Colourings
The kcolouring reconfiguration problem asks whether, for a given graph ...
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Stochastic Runtime Analysis of a Cross Entropy Algorithm for Traveling Salesman Problems
This article analyzes the stochastic runtime of a CrossEntropy Algorith...
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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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Explaining Predictions of NonLinear Classifiers in NLP
Layerwise relevance propagation (LRP) is a recently proposed technique ...
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SchNet: A continuousfilter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it...
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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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Context encoders as a simple but powerful extension of word2vec
With a simple architecture and the ability to learn meaningful word embe...
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Learning how to explain neural networks: PatternNet and PatternAttribution
DeConvNet, Guided BackProp, LRP, were invented to better understand deep...
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A statistical physics approach to learning curves for the Inverse Ising problem
Using methods of statistical physics, we analyse the error of learning c...
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NonDeterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
This paper investigates a type of instability that is linked to the gree...
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Changing Views on Curves and Surfaces
Visual events in computer vision are studied from the perspective of alg...
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Feature Importance Measure for Nonlinear Learning Algorithms
Complex problems may require sophisticated, nonlinear learning methods ...
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Approximate Bayes learning of stochastic differential equations
We introduce a nonparametric approach for estimating drift and diffusion...
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Optimal control for a robotic exploration, pickup and delivery problem
This paper addresses an optimal control problem for a robot that has to ...
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Signal Recovery from Unlabeled Samples
In this paper, we study the recovery of a signal from a set of noisy lin...
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Learning from Label Proportions in BrainComputer Interfaces: Online Unsupervised Learning with Guarantees
Objective: Using traditional approaches, a BrainComputer Interface (BCI...
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Berlin Institute of Technology (Technische Universität Berlin)
Homepage of the Technische Universität Berlin. TU Berlin sees itself as an internationally renowned university in the German capital, in the centre of Europe.