40 citations · 52 across the 9 of their papers we have counts for
11 papers
TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
Dayananda Herurkar, Federico Raue, Joachim Folz +2
Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and…
Hyperspherical Forward-Forward with Prototypical Representations
Shalini Sarode, Brian Moser, Joachim Folz +4
The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive in…
SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection
Brian B. Moser, Tobias C. Nauen, Arundhati S. Shanbhag +4
The goal of coreset selection is to identify representative subsets of datasets for efficient model training. Yet, existing approaches paradoxically require expensive training-base…
HyperCore: Coreset Selection under Noise via Hypersphere Models
Brian B. Moser, Arundhati S. Shanbhag, Tobias C. Nauen +4
The goal of coreset selection methods is to identify representative subsets of datasets for efficient model training. Yet, existing methods often ignore the possibility of annotati…
A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
Brian B. Moser, Arundhati S. Shanbhag, Stanislav Frolov +3
Coreset selection targets the challenge of finding a small, representative subset of a large dataset that preserves essential patterns for effective machine learning. Although seve…
Training Deep Neural Networks Without Batch Normalization
Divya Gaur, Joachim Folz, Andreas Dengel
Training neural networks is an optimization problem, and finding a decent set of parameters through gradient descent can be a difficult task. A host of techniques has been develope…
P NP, at least in Visual Question Answering
Shailza Jolly, Sebastian Palacio, Joachim Folz +3
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets. One of the most widely-used of these is the…
What do Deep Networks Like to See?
Sebastian Palacio, Joachim Folz, Jörn Hees +3
We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned…
Adversarial Defense based on Structure-to-Signal Autoencoders
Joachim Folz, Sebastian Palacio, Joern Hees +2
Adversarial attack methods have demonstrated the fragility of deep neural networks. Their imperceptible perturbations are frequently able fool classifiers into potentially dangerou…
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
Benjamin Bischke, Patrick Helber, Joachim Folz +2
The increased availability of high resolution satellite imagery allows to sense very detailed structures on the surface of our planet. Access to such information opens up new direc…
An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia
Jörn Hees, Rouven Bauer, Joachim Folz +2
Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. F…