1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
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…
cs.LG2024★ 1 cited
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…
cs.LG2022★ 1 cited
Looking at the posterior: accuracy and uncertainty of neural-network predictions
H. Linander, O. Balabanov, H. Yang +1
Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output. By looking at these posteri…