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Correction Crossref 13 open · 13 total · 0 disputed
DOI
10.1007/978-4-431-55978-8
Notice DOI
10.1007/978-4-431-55978-8_14
Event date
2020-10-21
Machine twin
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01One-hop citing occurrences

Correction Open
Understanding Latent Diffusability via Fisher Geometry

ref [1] · 2604.02751 · notice #7190 · dispute

Raw extraction · citation context

Despite these empirical advances, the underlying causes of latent diffusion failure remain theoretically obscured. Crucially, the literature lacks a formal, quantitative framework for measuring how "diffusible" a latent space isbefore committing to the computationally expensive training of a full diffusion model. In this paper, we demystify latent-space diffusability through the rigorous lens of Fisher geometry [1], complementing recent information-geometric perspectives on diffusion trajectories [ 13]. We analyze diffusability viadenoising complexity, quantified by the rate of change of the Minimum Mean Squared Error (MMSE) along the continuous diffusion trajectory. We show that this denoising complexity fundamentally decomposes into two terms: the Fisher Information (FI) and its rate of dissipation, which we define as the Fisher Information Rate (FIR).

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Despite these empirical advances, the underlying causes of latent diffusion failure remain theoretically obscured. Crucially, the literature lacks a formal, quantitative framework for measuring how "diffusible" a latent space isbefore committing to the computationally expensive training of a full diffusion model. In this paper, we demystify latent-space diffusability through the rigorous lens of Fisher geometry [1], complementing recent information-geometric perspectives on diffusion trajectories [ 13]. We analyze diffusability viadenoising complexity, quantified by the rate of change of the Minimum Mean Squared Error (MMSE) along the continuous diffusion trajectory. We show that this denoising complexity fundamentally decomposes into two terms: the Fisher Information (FI) and its rate of dissipation, which we define as the Fisher Information Rate (FIR)

Correction Open
The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability

ref [1] · 2604.24083 · notice #7187 · dispute

Raw extraction · bibliography line

doi: 10.1007/978-4-431-55978-8. Karl Friston. The free-energy principle: A unified brain theory?Nature Reviews Neu- roscience, 11(2):127–138,

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doi: 10.1007/978-4-431-55978-8. Karl Friston. The free-energy principle: A unified brain theory?Nature Reviews Neu- roscience, 11(2):127–138

Correction Open
Bayesian updates from coalgebraic determinisation

ref [70] · 2607.00034 · notice #7198 · dispute

Raw extraction · bibliography line

Amari, Shun-ichi , year = 2016, series =. Information. doi:10.1007/978-4-431-55978-8 , urldate =

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Amari, Shun-ichi, year = 2016, series =. Information. doi:10.1007/978-4-431-55978-8, urldate =

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