3 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
Niccolò Grillo, Andrea Toccaceli, Joël Mathys +3
Despite incredible progress, many neural architectures fail to properly generalize beyond their training distribution. As such, learning to reason in a correct and generalizable wa…
PUZZLES: A Benchmark for Neural Algorithmic Reasoning
Benjamin Estermann, Luca A. Lanzendörfer, Yannick Niedermayr +1
Algorithmic reasoning is a fundamental cognitive ability that plays a pivotal role in problem-solving and decision-making processes. Reinforcement Learning (RL) has demonstrated re…
SUPClust: Active Learning at the Boundaries
Yuta Ono, Till Aczel, Benjamin Estermann +1
Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel acti…
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
Paul Doucet, Benjamin Estermann, Till Aczel +1
This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained…
DAVA: Disentangling Adversarial Variational Autoencoder
Benjamin Estermann, Roger Wattenhofer
The use of well-disentangled representations offers many advantages for downstream tasks, e.g. an increased sample efficiency, or better interpretability. However, the quality of d…
Robust Disentanglement of a Few Factors at a Time
Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik
Disentanglement is at the forefront of unsupervised learning, as disentangled representations of data improve generalization, interpretability, and performance in downstream tasks.…