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Deep Residual Networks Learn Geodesic Curves in Wasserstein Space

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper proposes that deep residual networks (ResNets) learn the geodesic curve within Wasserstein space during training. The research models ResNet forward propagation using continuity equations, suggesting that ResNets with L2 regularization approximate this geodesic curve more effectively than plain networks. This improved approximation is posited as a reason for ResNets' better optimization and generalization capabilities. AI

IMPACT This research offers theoretical insights into the optimization and generalization of ResNets, potentially informing future network architectures.

RANK_REASON The cluster contains a single academic paper detailing a theoretical finding about deep neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Residual Networks Learn Geodesic Curves in Wasserstein Space

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The cluster contains a single academic paper detailing a theoretical finding about deep neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Kuo Gai, Shihua Zhang ·

    Deep Residual Networks Learn the Geodesic Curve in the Wasserstein Space

    arXiv:2102.09235v3 Announce Type: replace-cross Abstract: Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNNs, especially deep residual networks (ResNets), tend to learn during train…

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