20 citations · 21 across the 6 of their papers we have counts for
8 papers
Enhancing Tabular Learners with Context-Aware Semantic Embeddings
Günther Schindler, Maximilian Schambach, Johannes Höhne
While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the ric…
TabGemma: Text-Based Tabular ICL via LLM using Continued Pretraining and Retrieval
Günther Schindler, Maximilian Schambach, Michael Medek +1
We study LLMs for tabular prediction with mixed text, numeric, and categorical fields. We introduce TabGemma, a schema-agnostic in-context learner that treats rows as sequences and…
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks
Wolfgang Roth, Günther Schindler, Holger Fröning +1
We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator…
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement
Lukas Pfeifenberger, Matthias Zöhrer, Günther Schindler +3
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches. This need for…
On the Difficulty of Designing Processor Arrays for Deep Neural Networks
Kevin Stehle, Günther Schindler, Holger Fröning
Systolic arrays are a promising computing concept which is in particular inline with CMOS technology trends and linear algebra operations found in the processing of artificial neur…
Resource-Efficient Neural Networks for Embedded Systems
Wolfgang Roth, Günther Schindler, Bernhard Klein +5
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-e…
Parameterized Structured Pruning for Deep Neural Networks
Guenther Schindler, Wolfgang Roth, Franz Pernkopf +1
As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantizatio…
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…