b'Bernhard Sick'

research

∙ 05/24/2023

Sampling-based Uncertainty Estimation for an Instance Segmentation Network

The examination of uncertainty in the predictions of machine learning (M...

0 Florian Heidecker, et al. ∙

research

∙ 05/09/2023

Dataset of a parameterized U-bend flow for Deep Learning Applications

This dataset contains 10,000 fluid flow and heat transfer simulations in...

0 Jens Decke, et al. ∙

research

∙ 04/29/2023

Sensor Equivariance by LiDAR Projection Images

In this work, we propose an extension of conventional image data by an a...

0 Hannes Reichert, et al. ∙

research

∙ 04/05/2023

Multi-annotator Deep Learning: A Probabilistic Framework for Classification

Solving complex classification tasks using deep neural networks typicall...

0 Marek Herde, et al. ∙

research

∙ 10/17/2022

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory data analysis is an essential component for highly automated ...

0 Kevin Rösch, et al. ∙

research

∙ 10/12/2022

Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning

Retraining deep neural networks when new data arrives is typically compu...

0 Marek Herde, et al. ∙

research

∙ 10/06/2022

A Review of Uncertainty Calibration in Pretrained Object Detectors

In the field of deep learning based computer vision, the development of ...

0 Denis Huseljic, et al. ∙

research

∙ 04/29/2022

Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast

Task embeddings in multi-layer perceptrons for multi-task learning and i...

0 Jens Schreiber, et al. ∙

research

∙ 04/28/2022

Model Selection, Adaptation, and Combination for Deep Transfer Learning through Neural Networks in Renewable Energies

There is recent interest in using model hubs, a collection of pre-traine...

6 Jens Schreiber, et al. ∙

research

∙ 04/01/2022

Synthetic Photovoltaic and Wind Power Forecasting Data

Photovoltaic and wind power forecasts in power systems with a high share...

0 Stephan Vogt, et al. ∙

research

∙ 02/14/2022

Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning

Continual learning can enable neural networks to evolve by learning new ...

0 Yujiang He, et al. ∙

research

∙ 09/23/2021

A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

Pool-based active learning (AL) aims to optimize the annotation process ...

4 Marek Herde, et al. ∙

research

∙ 09/20/2021

Description of Corner Cases in Automated Driving: Goals and Challenges

Scaling the distribution of automated vehicles requires handling various...

0 Daniel Bogdoll, et al. ∙

research

∙ 08/31/2021

Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses

X-ray free-electron lasers (XFELs) as the world`s most brilliant light s...

0 Kristina Dingel, et al. ∙

research

∙ 08/09/2021

Probabilistic Active Learning for Active Class Selection

In machine learning, active class selection (ACS) algorithms aim to acti...

14 Daniel Kottke, et al. ∙

research

∙ 06/30/2021

Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information

This article presents a novel approach to incorporate visual cues from v...

0 Stefan Zernetsch, et al. ∙

research

∙ 06/04/2021

Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories

In this article, an approach for probabilistic trajectory forecasting of...

0 Viktor Kress, et al. ∙

research

∙ 05/14/2021

Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems

Full-stack autonomous driving perception modules usually consist of data...

0 Hannes Reichert, et al. ∙

research

∙ 05/04/2021

Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders

Deep neural networks often suffer from overconfidence which can be partl...

0 Felix Möller, et al. ∙

research

∙ 04/19/2021

Cyclist Intention Detection: A Probabilistic Approach

This article presents a holistic approach for probabilistic cyclist inte...

0 Stefan Zernetsch, et al. ∙

research

∙ 03/05/2021

An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Systems and functions that rely on machine learning (ML) are the basis o...

0 Florian Heidecker, et al. ∙

research

∙ 01/04/2021

CLeaR: An Adaptive Continual Learning Framework for Regression Tasks

Catastrophic forgetting means that a trained neural network model gradua...

0 Yujiang He, et al. ∙

research

∙ 09/29/2020

Quantile Surfaces – Generalizing Quantile Regression to Multivariate Targets

In this article, we present a novel approach to multivariate probabilist...

0 Maarten Bieshaar, et al. ∙

research

∙ 09/29/2020

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Support Vector Data Description (SVDD) is a popular one-class classifier...

7 Adrian Englhardt, et al. ∙

research

∙ 06/09/2020

Off-the-shelf sensor vs. experimental radar – How much resolution is necessary in automotive radar classification?

Radar-based road user detection is an important topic in the context of ...

0 Nicolas Scheiner, et al. ∙

research

∙ 06/02/2020

Toward Optimal Probabilistic Active Learning Using a Bayesian Approach

Gathering labeled data to train well-performing machine learning models ...

55 Daniel Kottke, et al. ∙

research

∙ 04/29/2020

Emerging Relation Network and Task Embedding for Multi-Task Regression Problems

Multi-task learning (mtl) provides state-of-the-art results in many appl...

0 Jens Schreiber, et al. ∙

research

∙ 04/29/2020

Extended Coopetitive Soft Gating Ensemble

This article is about an extension of a recent ensemble method called Co...

0 Stephan Deist, et al. ∙

research

∙ 02/07/2020

Trust Your Model: Iterative Label Improvement and Robust Training by Confidence Based Filtering and Dataset Partitioning

State-of-the-art, high capacity deep neural networks not only require la...

0 Christian Haase-Schütz, et al. ∙

research

∙ 12/13/2019

Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

Conventional sensor systems record information about directly visible ob...

38 Nicolas Scheiner, et al. ∙

research

∙ 07/08/2019

A Multi-Stage Clustering Framework for Automotive Radar Data

Radar sensors provide a unique method for executing environmental percep...

0 Nicolas Scheiner, et al. ∙

research

∙ 06/03/2019

Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic

For the integration of renewable energy sources, power grid operators ne...

0 Jens Schreiber, et al. ∙

research

∙ 05/31/2019

Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models

Despite the increasing importance of forecasts of renewable energy, curr...

0 Jens Schreiber, et al. ∙

research

∙ 05/28/2019

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challengi...

0 Nicolas Scheiner, et al. ∙

research

∙ 05/28/2019

Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices

Baseline generation for tracking applications is a difficult task when w...

0 Nicolas Scheiner, et al. ∙

research

∙ 05/27/2019

Radar-based Feature Design and Multiclass Classification for Road User Recognition

The classification of individual traffic participants is a complex task,...

0 Nicolas Scheiner, et al. ∙

research

∙ 05/27/2019

Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS

Annotating automotive radar data is a difficult task. This article prese...

0 Nicolas Scheiner, et al. ∙

research

∙ 05/16/2019

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

The field of collaborative interactive learning (CIL) aims at developing...

0 Tom Hanika, et al. ∙

research

∙ 01/29/2019

Limitations of Assessing Active Learning Performance at Runtime

Classification algorithms aim to predict an unknown label (e.g., a quali...

0 Daniel Kottke, et al. ∙

research

∙ 09/11/2018

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence

Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an...

0 Maarten Bieshaar, et al. ∙

research

∙ 08/14/2018

Quantifying the Influences on Probabilistic Wind Power Forecasts

In recent years, probabilistic forecasts techniques were proposed in res...

0 Jens Schreiber, et al. ∙

research

∙ 08/08/2018

Starting Movement Detection of Cyclists Using Smart Devices

In near future, vulnerable road users (VRUs) such as cyclists and pedest...

0 Maarten Bieshaar, et al. ∙

research

∙ 07/03/2018

Coopetitive Soft Gating Ensemble

In this article, we proposed the Coopetititve Soft Gating Ensemble or CS...

0 Stephan Deist, et al. ∙

research

∙ 03/16/2018

A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation

In this article, we propose a novel ensemble technique with a multi-sche...

0 André Gensler, et al. ∙

research

∙ 03/09/2018

Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning

Avoiding collisions with vulnerable road users (VRUs) using sensor-based...

0 Michael Goldhammer, et al. ∙

research

∙ 03/09/2018

Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble

In future, vehicles and other traffic participants will be interconnecte...

0 Maarten Bieshaar, et al. ∙

research

∙ 03/09/2018

Highly Automated Learning for Improved Active Safety of Vulnerable Road Users

Highly automated driving requires precise models of traffic participants...

0 Maarten Bieshaar, et al. ∙

research

∙ 03/06/2018

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

In this article, we present a novel approach to detect starting motions ...

0 Stefan Zernetsch, et al. ∙

research

∙ 03/06/2018

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure

In future traffic scenarios, vehicles and other traffic participants wil...

0 Günther Reitberger, et al. ∙

research

∙ 01/30/2017

Self-Adaptation of Activity Recognition Systems to New Sensors

Traditional activity recognition systems work on the basis of training, ...

0 David Bannach, et al. ∙

Bernhard Sick

Featured Co-authors