Technische Universität Darmstadt

research

∙ 03/04/2022

A Typology to Explore and Guide Explanatory Interactive Machine Learning

Recently, more and more eXplanatory Interactive machine Learning (XIL) m...

Felix Friedrich, et al. ∙

research

∙ 10/07/2021

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming

The goal of combining the robustness of neural networks and the expressi...

Arseny Skryagin, et al. ∙

research

∙ 05/10/2020

Variational Clustering: Leveraging Variational Autoencoders for Image Clustering

Recent advances in deep learning have shown their ability to learn stron...

Vignesh Prasad, et al. ∙

research

∙ 07/11/2020

Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning

General-purpose trajectory planning algorithms for automated driving uti...

Sascha Rosbach, et al. ∙

research

∙ 04/21/2020

How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking

We present a novel multi-attentional convolutional architecture to tackl...

Isidoros Marougkas, et al. ∙

research

∙ 09/09/2021

Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning

Recent prompt-based approaches allow pretrained language models to achie...

Prasetya Ajie Utama, et al. ∙

research

∙ 11/29/2018

Robust Bayesian Cluster Enumeration

A major challenge in cluster analysis is that the number of data cluster...

Freweyni K. Teklehaymanot, et al. ∙

research

∙ 06/14/2022

Attributions Beyond Neural Networks: The Linear Program Case

Linear Programs (LPs) have been one of the building blocks in machine le...

Florian Peter Busch, et al. ∙

research

∙ 03/08/2022

Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery

Robot assembly discovery is a challenging problem that lives at the inte...

Niklas Funk, et al. ∙

research

∙ 06/14/2022

Machines Explaining Linear Programs

There has been a recent push in making machine learning models more inte...

David Steinmann, et al. ∙

research

∙ 09/14/2021

Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits

Probabilistic circuits (PCs) have become the de-facto standard for learn...

Zhongjie Yu, et al. ∙

research

∙ 04/22/2022

Balancing Expert Utilization in Mixture-of-Experts Layers Embedded in CNNs

This work addresses the problem of unbalanced expert utilization in spar...

Svetlana Pavlitskaya, et al. ∙

research

∙ 07/15/2019

Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

The performance of deep network learning strongly depends on the choice ...

Alejandro Molina, et al. ∙

research

∙ 03/29/2022

Finding Structure and Causality in Linear Programs

Linear Programs (LP) are celebrated widely, particularly so in machine l...

Matej Zečević, et al. ∙

research

∙ 10/08/2021

Inferring Offensiveness In Images From Natural Language Supervision

Probing or fine-tuning (large-scale) pre-trained models results in state...

Patrick Schramowski, et al. ∙

research

∙ 07/26/2021

Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation

The Fiedler vector of a connected graph is the eigenvector associated wi...

Aylin Tastan, et al. ∙

research

∙ 03/09/2022

Regularized Deep Signed Distance Fields for Reactive Motion Generation

Autonomous robots should operate in real-world dynamic environments and ...

Puze Liu, et al. ∙

research

∙ 12/19/2018

TD-Regularized Actor-Critic Methods

Actor-critic methods can achieve incredible performance on difficult rei...

Simone Parisi, et al. ∙

research

∙ 08/08/2019

On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

Recently, several authors have advocated the use of rule learning algori...

Michael Rapp, et al. ∙

research

∙ 02/03/2020

CryptoSPN: Privacy-preserving Sum-Product Network Inference

AI algorithms, and machine learning (ML) techniques in particular, are i...

Amos Treiber, et al. ∙

research

∙ 06/12/2020

SAMBA: Safe Model-Based Active Reinforcement Learning

In this paper, we propose SAMBA, a novel framework for safe reinforcemen...

Alexander I. Cowen-Rivers, et al. ∙

research

∙ 07/04/2019

Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

Assistive robots can potentially improve the quality of life and persona...

Susanne Trick, et al. ∙

research

∙ 06/18/2019

Declarative Learning-Based Programming as an Interface to AI Systems

Data-driven approaches are becoming more common as problem-solving techn...

Parisa Kordjamshidi, et al. ∙

research

∙ 07/04/2020

Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization

3D scene models are useful in robotics for tasks such as path planning, ...

Mikko Lauri, et al. ∙

research

∙ 01/28/2022

Plug Play Attacks: Towards Robust and Flexible Model Inversion Attacks

Model inversion attacks (MIAs) aim to create synthetic images that refle...

Lukas Struppek, et al. ∙

research

∙ 07/06/2019

Entropic Regularization of Markov Decision Processes

An optimal feedback controller for a given Markov decision process (MDP)...

Boris Belousov, et al. ∙

research

∙ 10/05/2021

Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domain...

Michael Lutter, et al. ∙

research

∙ 04/01/2022

Robust and Efficient Aggregation for Distributed Learning

Distributed learning paradigms, such as federated and decentralized lear...

Stefan Vlaski, et al. ∙

research

∙ 11/13/2017

A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization

In this paper, we propose a convergent parallel best-response algorithm ...

Yang Yang, et al. ∙

research

∙ 09/21/2017

Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learnin...

Maximilian Hüttenrauch, et al. ∙

research

∙ 10/22/2017

A Novel Bayesian Cluster Enumeration Criterion for Unsupervised Learning

The Bayesian Information Criterion (BIC) has been widely used for estima...

Freweyni K. Teklehaymanot, et al. ∙

research

∙ 02/26/2017

Bayesian Nonparametric Unmixing of Hyperspectral Images

Hyperspectral imaging is an important tool in remote sensing, allowing f...

Jürgen Hahn, et al. ∙

research

∙ 02/26/2017

Bayesian Nonparametric Feature and Policy Learning for Decision-Making

Learning from demonstrations has gained increasing interest in the recen...

Jürgen Hahn, et al. ∙

research

∙ 05/04/2016

A Bayesian Approach to Policy Recognition and State Representation Learning

Learning from demonstration (LfD) is the process of building behavioral ...

Adrian Šošić, et al. ∙

research

∙ 05/17/2014

That's sick dude!: Automatic identification of word sense change across different timescales

In this paper, we propose an unsupervised method to identify noun sense ...

Sunny Mitra, et al. ∙

research

∙ 02/06/2016

DOLPHIn - Dictionary Learning for Phase Retrieval

We propose a new algorithm to learn a dictionary for reconstructing and ...

Andreas M. Tillmann, et al. ∙

research

∙ 12/04/2015

Model Validation for Vision Systems via Graphics Simulation

Rapid advances in computation, combined with latest advances in computer...

V S R Veeravasarapu, et al. ∙

research

∙ 12/03/2015

Simulations for Validation of Vision Systems

As the computer vision matures into a systems science and engineering di...

V S R Veeravasarapu, et al. ∙

research

∙ 07/12/2012

Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version

Rich and complex time-series data, such as those generated from engineer...

Marc Peter Deisenroth, et al. ∙

research

∙ 07/20/2017

From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems

Task selection in micro-task markets can be supported by recommender sys...

Steffen Schnitzer, et al. ∙

research

∙ 12/11/2017

I Trust my Zombies: A Trust-enabled Botnet

Defending against botnets has always been a cat and mouse game. Cyber-se...

Emmanouil Vasilomanolakis, et al. ∙

research

∙ 12/19/2017

Mixin Composition Synthesis based on Intersection Types

We present a method for synthesizing compositions of mixins using type i...

Jan Bessai, et al. ∙

research

∙ 10/17/2017

Solving nonlinear circuits with pulsed excitation by multirate partial differential equations

In this paper the concept of Multirate Partial Differential Equations (M...

Andreas Pels, et al. ∙

research

∙ 09/18/2017

Recent Advances of Isogeometric Analysis in Computational Electromagnetics

In this communication the advantages and drawbacks of the isogeometric a...

Zeger Bontinck, et al. ∙

research

∙ 12/14/2017

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the es...

Michail-Antisthenis I. Tsompanas, et al. ∙

research

∙ 11/27/2017

The Status of Quantum-Based Long-Term Secure Communication over the Internet

Sensitive digital data, such as health information or governmental archi...

Matthias Geihs, et al. ∙

research

∙ 10/02/2017

CrySL: Validating Correct Usage of Cryptographic APIs

Various studies have empirically shown that the majority of Java and And...

Stefan Krüger, et al. ∙

research

∙ 08/25/2017

Gap-planar Graphs

We introduce the family of k-gap-planar graphs for k ≥ 0, i.e., graphs t...

Sang Won Bae, et al. ∙

research

∙ 09/04/2017

Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors

While visual comparison of directed acyclic graphs (DAGs) is commonly en...

Kathrin Ballweg, et al. ∙

research

∙ 11/13/2017

Beyond the Hype: On Using Blockchains in Trust Management for Authentication

Trust Management (TM) systems for authentication are vital to the securi...

Nikolaos Alexopoulos, et al. ∙

Technische Universität Darmstadt

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