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Equivariance and Augmentation for Bayesian Neural Networks

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arXiv:2606. 26273v1 Announce Type: new Abstract: Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging.

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Authors: Miaowen Dong, Axel Flinth, Jan E. Gerken
Released: 2026-06-26 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Equivariance and Augmentation for Bayesian Neural Networks overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, under, line, control, trained, symmetry. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.26273

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