6 papers
Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks
Longde Huang, Oleksandr Balabanov, Hampus Linander +3
Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context f…
PEAR: Equal Area Weather Forecasting on the Sphere
Hampus Linander, Tage Tykesson, Pietro Rosso +3
Artificial intelligence is rapidly reshaping the natural sciences, with weather forecasting emerging as a flagship AI4Science application where machine learning models can now riva…
Soft Geometric Inductive Bias for Object Centric Dynamics
Hampus Linander, Conor Heins, Alexander Tschantz +2
Equivariance is a powerful prior for learning physical dynamics, yet exact group equivariance can degrade performance if the symmetries are broken. We propose object-centric world…
AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models
Conor Heins, Toon Van de Maele, Alexander Tschantz +11
Current deep reinforcement learning (DRL) approaches achieve state-of-the-art performance in various domains, but struggle with data efficiency compared to human learning, which le…
Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
Oleksandr Balabanov, Hampus Linander
Fine-tuning large language models can improve task specific performance, although a general understanding of what the fine-tuned model has learned, forgotten and how to trust its p…
Bayesian Predictive Coding
Alexander Tschantz, Magnus Koudahl, Hampus Linander +4
Predictive coding (PC) is an influential theory of information processing in the brain, providing a biologically plausible alternative to backpropagation. It is motivated in terms…