191 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Volkswagen Group (Germany)DE10 papers
- Technical University of MunichDE6 papers
- Data:Lab Munich (Germany)DE4 papers
- Ludwig-Maximilians-Universität MünchenDE3 papers
- Leiden UniversityNL2 papers
- OTH RegensburgDE2 papers
- Barcelona Institute of Science and TechnologyES1 paper
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE1 paper
- Freie Universität BerlinDE1 paper
- Institute of Photonic SciencesES1 paper
- Institute of Science and Technology1 paper
- Johns Hopkins UniversityUS1 paper
24 papers
SelfOccFlow: Towards end-to-end self-supervised 3D Occupancy Flow prediction
Xavier Timoneda, Markus Herb, Fabian Duerr +1
Estimating 3D occupancy and motion at the vehicle's surroundings is essential for autonomous driving, enabling situational awareness in dynamic environments. Existing approaches jo…
Latent Matters: Learning Deep State-Space Models
Alexej Klushyn, Richard Kurle, Maximilian Soelch +2
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower b…
KhabarChin: Automatic Detection of Important News in the Persian Language
Hamed Hematian Hemati, Arash Lagzian, Moein Salimi Sartakhti +2
Being aware of important news is crucial for staying informed and making well-informed decisions efficiently. Natural Language Processing (NLP) approaches can significantly automat…
Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks
Laura Fieback, Bidya Dash, Jakob Spiegelberg +1
Quantifying predictive uncertainty of deep semantic segmentation networks is essential in safety-critical tasks. In applications like autonomous driving, where video data is availa…
Initial state preparation for quantum chemistry on quantum computers
Stepan Fomichev, Kasra Hejazi, Modjtaba Shokrian Zini +8
Quantum algorithms for ground-state energy estimation of chemical systems require a high-quality initial state. However, initial state preparation is commonly either neglected enti…
Deep-Circuit QAOA
Gereon Koßmann, Lennart Binkowski, Lauritz van Luijk +2
Despite its popularity, several empirical and theoretical studies suggest that the quantum approximate optimization algorithm (QAOA) has persistent issues in providing a substantia…
Hybrid quantum ResNet for car classification and its hyperparameter optimization
Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin +6
Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of milli…
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Francesco Romor, Marco Tezzele, Markus Mrosek +2
Multi-fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the ap…
Radar-based Automotive Localization using Landmarks in a Multimodal Sensor Graph-based Approach
Stefan Jürgens, Niklas Koch, Marc-Michael Meinecke
Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative locali…
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison
Lukas Brunke, Prateek Agrawal, Nikhil George
Input perturbation methods occlude parts of an input to a function and measure the change in the function's output. Recently, input perturbation methods have been applied to genera…
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Justin Bayer, Maximilian Soelch, Atanas Mirchev +2
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often on…
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
Kira Maag, Matthias Rottmann, Serin Varghese +3
Instance segmentation with neural networks is an essential task in environment perception. In many works, it has been observed that neural networks can predict false positive insta…
Quantum Shuttle: Traffic Navigation with Quantum Computing
Sheir Yarkoni, Florian Neukart, Eliane Moreno Gomez Tagle +5
The Web Summit conference in Lisbon, Portugal, is one of the biggest technology conferences in Europe, attended by tens of thousands of people every year. The high influx of people…
Semi-supervised time series classification method for quantum computing
Sheir Yarkoni, Andrii Kleshchonok, Yury Dzerin +2
In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of rec…
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt +1
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages…
Learning Flat Latent Manifolds with VAEs
Nutan Chen, Alexej Klushyn, Francesco Ferroni +2
Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, w…
Learning Hierarchical Priors in VAEs
Alexej Klushyn, Nutan Chen, Richard Kurle +2
We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incenti…
Assessing Solution Quality of 3SAT on a Quantum Annealing Platform
Thomas Gabor, Sebastian Zielinski, Sebastian Feld +6
When solving propositional logic satisfiability (specifically 3SAT) using quantum annealing, we analyze the effect the difficulty of different instances of the problem has on the q…
A Hybrid Solution Method for the Capacitated Vehicle Routing Problem Using a Quantum Annealer
Sebastian Feld, Christoph Roch, Thomas Gabor +5
The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that has been of great interest for decades for both, science and industry. The CVRP is a variant…
Active Learning based on Data Uncertainty and Model Sensitivity
Nutan Chen, Alexej Klushyn, Alexandros Paraschos +2
Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether th…
Approximate Bayesian inference in spatial environments
Atanas Mirchev, Baris Kayalibay, Maximilian Soelch +2
Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems suc…
Two-Stream RNN/CNN for Action Recognition in 3D Videos
Rui Zhao, Haider Ali, Patrick van der Smagt
The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes. Despite advances in sensing, in particul…
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Maximilian Karl, Maximilian Soelch, Justin Bayer +1
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in…
Dynamic Programming on Nominal Graphs
Nicklas Hoch, Ugo Montanari, Matteo Sammartino
Many optimization problems can be naturally represented as (hyper) graphs, where vertices correspond to variables and edges to tasks, whose cost depends on the values of the adjace…