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New papers analyze learning regimes in bottleneck and linear autoencoders

By PulseAugur Editorial · [4 sources] ·

Researchers have published two papers analyzing different learning regimes in autoencoders. One paper focuses on nonlinear autoencoders with a bottleneck, deriving mean-field learning dynamics and showing finite networks can approximate infinite-width solutions. The other paper proposes a framework for understanding extreme learning regimes in large linear autoencoders, identifying five distinct regimes and deriving loss evolutions for four of them. AI

IMPACT Provides theoretical grounding for understanding autoencoder behavior, potentially guiding future model development and optimization.

RANK_REASON Two academic papers published on arXiv detailing theoretical analyses of autoencoder learning regimes.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Santanu Das, Ramyak Bilas, Pascal Esser, Satyaki Mukherjee ·

    Beyond Linear and Overcomplete Regimes: A Mean-Field Analysis of Bottleneck Autoencoders

    arXiv:2606.07120v1 Announce Type: new Abstract: Autoencoders (AEs) learn low-dimensional representations by mapping data into a latent space while minimizing reconstruction error. Despite their empirical success, theoretical understanding remains limited and largely restricted to…

  2. arXiv cs.LG TIER_1 English(EN) · Satyaki Mukherjee ·

    Beyond Linear and Overcomplete Regimes: A Mean-Field Analysis of Bottleneck Autoencoders

    Autoencoders (AEs) learn low-dimensional representations by mapping data into a latent space while minimizing reconstruction error. Despite their empirical success, theoretical understanding remains limited and largely restricted to linear models or settings without a bottleneck.…

  3. arXiv stat.ML TIER_1 English(EN) · Eugene Golikov, Yaroslav Gusev, Dmitry Yarotsky ·

    A prism hierarchy of learning regimes in large linear autoencoders

    arXiv:2606.05335v1 Announce Type: cross Abstract: Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becomes theoretically tractable. It is, however, desirable to have a systematically obt…

  4. arXiv stat.ML TIER_1 English(EN) · Dmitry Yarotsky ·

    A prism hierarchy of learning regimes in large linear autoencoders

    Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becomes theoretically tractable. It is, however, desirable to have a systematically obtained picture of all qualitatively different extre…

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