3 citations · 5 across the 9 of their papers we have counts for
9 papers
Reasoning Effort and Problem Complexity: A Scaling Analysis in LLMs
Benjamin Estermann, Roger Wattenhofer
Large Language Models (LLMs) have demonstrated remarkable text generation capabilities, and recent advances in training paradigms have led to breakthroughs in their reasoning perfo…
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…
What Determines the Price of NFTs?
Vivian Ziemke, Benjamin Estermann, Roger Wattenhofer +1
In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology. NFTs serve as the foundat…
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…
Abstract Visual Reasoning Enabled by Language
Giacomo Camposampiero, Loic Houmard, Benjamin Estermann +2
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexi…
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.…