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Constructing VAE Latent Spaces with Prescribed Topology

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arXiv:2606. 07058v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) learn low-dimensional latent representations of high-dimensional data.

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Authors: Jilles S. van Hulst, Jakub M. Tomczak, W. P. M. H. Heemels, Duarte J. Antunes
Released: 2026-06-08 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Constructing VAE Latent Spaces with Prescribed Topology 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, class, circle, soft, line, project, factor, symmetry. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.07058

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