Search Results for author:
Found 32 papers, 7 papers with code
RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate.
auto-sktime: Automated Time Series Forecasting
The framework employs Bayesian optimization, to automatically construct pipelines from statistical, machine learning (ML) and deep neural network (DNN) models.
Improving the Effectiveness of Deep Generative Data
Although looking appealing to human eyes, training a model on purely synthetic images for downstream image processing tasks like image classification often results in an undesired performance drop compared to training on real data.
Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development
In this work we merge recent regulation efforts by the European Union and first proposals for AI guidelines with recent trends in research: data and model cards.
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning
The pipeline consists of a general machine learning model for hand pose estimation trained on a generalized dataset, spatial and temporal filtering to account for anatomical constraints of the hand, and a retraining step to improve the model.
Automated Machine Learning for Remaining Useful Life Predictions
Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required.
Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning
In recent years, the development of Artificial Intelligence (AI) has shown tremendous potential in diverse areas.
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches.
Mixture of Decision Trees for Interpretable Machine Learning
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT).
Kalman-Bucy-Informed Neural Network for System Identification
Identifying parameters in a system of nonlinear, ordinary differential equations is vital for designing a robust controller.
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules
Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set.
Radar-based Respiratory Rate Monitoring in Standing Position
Estimating human vital signs in a contactless non-invasive method using radar provides a convenient method in the medical field to conduct several health checkups easily and quickly.
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
Even though such automatically synthesized ML pipelines are able to achieve a competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines.
Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder
In this work, we present a deep residual 3D autoencoder based on the EfficientNet architecture, intended for transfer learning tasks related to 3D CAD model assessment.
A MIMO Radar-Based Metric Learning Approach for Activity Recognition
In this paper, a MIMO radar is used to formulate a novel micro-motion spectrogram for the angular velocity ({\mu}-{\omega}) in non-tangential scenarios.
A MIMO Radar-based Few-Shot Learning Approach for Human-ID
Radar for deep learning-based human identification has become a research area of increasing interest.
Kalman Bayesian Neural Networks for Closed-form Online Learning
Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i. e., the weights.
Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers
This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes.
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems
Maintenance scheduling is a complex decision-making problem in the production domain, where a number of maintenance tasks and resources has to be assigned and scheduled to production entities in order to prevent unplanned production downtime.
Towards Measuring Bias in Image Classification
For this purpose, first an artificial dataset with a known bias is created and used to train intentionally biased CNNs.
DimRad: A Radar-Based Perception System for Prosthetic Leg Barrier Traversing
The algorithm is implemented on a microcontroller subsystem of the radar kit to qualify the perception system for embedded integration in powered prosthetic legs.
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation
Single shot approaches have demonstrated tremendous success on various computer vision tasks.
Incremental Search Space Construction for Machine Learning Pipeline Synthesis
We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.
Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes
In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images.
A Survey on the Explainability of Supervised Machine Learning
This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML).
Bayesian Perceptron: Towards fully Bayesian Neural Networks
In this paper a novel approach towards fully Bayesian NNs is proposed, where training and predictions of a perceptron are performed within the Bayesian inference framework in closed-form.
Single Shot 6D Object Pose Estimation
In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images.
Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking
In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking.
Deep Workpiece Region Segmentation for Bin Picking
For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor.
Benchmark and Survey of Automated Machine Learning Frameworks
This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets.
Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization
One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability.
Robust Filtering and Smoothing with Gaussian Processes
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models.
