20 citations · 21 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…