207 citations · 253 across the 25 of their papers we have counts for
30 papers
A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN
Robert Bitterling, Christian Nettersheim, Jörn Hees +1
Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning. Traditional (e…
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…
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
Dayananda Herurkar, Jörn Hees, Vesselin Tzvetkov +1
The remarkable success of Deep Learning approaches is often based and demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, o…
Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training
Mohammad Majd Saad Al Deen, Maren Pielka, Jörn Hees +2
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contr…
DartsReNet: Exploring new RNN cells in ReNet architectures
Brian Moser, Federico Raue, Jörn Hees +1
We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS. We are interested in the ReNet architec…
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
Timur Sattarov, Dayananda Herurkar, Jörn Hees
Recent advances in Explainable AI (XAI) increased the demand for deployment of safe and interpretable AI models in various industry sectors. Despite the latest success of deep neur…
DT2I: Dense Text-to-Image Generation from Region Descriptions
Stanislav Frolov, Prateek Bansal, Jörn Hees +1
Despite astonishing progress, generating realistic images of complex scenes remains a challenging problem. Recently, layout-to-image synthesis approaches have attracted much intere…
Spatial Transformer Networks for Curriculum Learning
Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio +3
Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the c…
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Fatemeh Azimi, Federico Raue, Joern Hees +1
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance. In this work, we combine the idea of ST…
AudioCLIP: Extending CLIP to Image, Text and Audio
Andrey Guzhov, Federico Raue, Jörn Hees +1
In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-spe…
XAI Handbook: Towards a Unified Framework for Explainable AI
Sebastian Palacio, Adriano Lucieri, Mohsin Munir +3
The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus re…
ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio
Andrey Guzhov, Federico Raue, Jörn Hees +1
Environmental Sound Classification (ESC) is a rapidly evolving field that recently demonstrated the advantages of application of visual domain techniques to the audio-related tasks…
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
Stanislav Frolov, Avneesh Sharma, Jörn Hees +3
Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-…
RECol: Reconstruction Error Columns for Outlier Detection
Jörn Hees, Dayananda Herurkar, Mario Meier
Detecting outliers or anomalies is a common data analysis task. As a sub-field of unsupervised machine learning, a large variety of approaches exist, but the vast majority treats t…
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
Sebastian Palacio, Philipp Engler, Jörn Hees +1
Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross-ent…
Adversarial Text-to-Image Synthesis: A Review
Stanislav Frolov, Tobias Hinz, Federico Raue +2
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way fo…
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
Stanislav Frolov, Shailza Jolly, Jörn Hees +1
Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds a…
ESResNet: Environmental Sound Classification Based on Visual Domain Models
Andrey Guzhov, Federico Raue, Jörn Hees +1
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches a…
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…
Fusion Strategies for Learning User Embeddings with Neural Networks
Philipp Blandfort, Tushar Karayil, Federico Raue +2
Growing amounts of online user data motivate the need for automated processing techniques. In case of user ratings, one interesting option is to use neural networks for learning to…
An Overview of Computational Approaches for Interpretation Analysis
Philipp Blandfort, Jörn Hees, Desmond U. Patton
It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an i…
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images
Tushar Karayil, Philipp Blandfort, Jörn Hees +1
Subjective visual interpretation is a challenging yet important topic in computer vision. Many approaches reduce this problem to the prediction of adjective- or attribute-labels fr…
Towards Semantically Enhanced Data Understanding
Markus Schröder, Christian Jilek, Jörn Hees +1
In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets. Such knowledge-intensive tasks require a lot of documentation,…
Simplified SPARQL REST API - CRUD on JSON Object Graphs via URI Paths
Markus Schröder, Jörn Hees, Ansgar Bernardi +3
Within the Semantic Web community, SPARQL is one of the predominant languages to query and update RDF knowledge. However, the complexity of SPARQL, the underlying graph structure a…