NAE: Normalizing AutoEncoder
We consider the setting of Normalizing flows with approximate inverses, an established paradigm spanning both full-dimensional ($d=D$) and bottleneck ($d<D$) settings, and group these models under the term flow autoencoders. We present a theoretical investigation into their training dynamics and prove that the proposed loss used by existing approaches is suboptimal; specifically, both encoder and decoder surrogates must be optimized in alignment with reconstruction loss. Guided by these insights
Record details
Published: 12 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “NAE: Normalizing AutoEncoder,” evidence record 19067, https://ethics.ai/record/19067 (originally published by arXiv cs.LG).
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