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Depth in neural networks induces implicit low-rank bias, study finds

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the implicit bias of depth in neural networks, specifically within the deep unconstrained feature model (UFM). Their analysis, focusing on gradient descent and depth without explicit regularization, reveals that depth inherently promotes a low-rank bias. This bias encourages solutions that deviate from standard neural collapse, aligning instead with max-margin solutions previously observed in width-bottlenecked networks. The study also identifies how spectral initialization influences singular values and characterizes the shrinking basin of attraction for neural collapse as depth increases. AI

IMPACT Provides theoretical insights into the behavior of deep neural networks, potentially influencing future model architectures and training methodologies.

RANK_REASON Academic paper published on arXiv detailing theoretical findings about neural network behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Depth in neural networks induces implicit low-rank bias, study finds

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Academic paper published on arXiv detailing theoretical findings about neural network behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Connall Garrod, Jonathan P. Keating, Christos Thrampoulidis ·

    The Implicit Bias of Depth: From Neural Collapse to Softmax Codes

    arXiv:2605.23087v1 Announce Type: new Abstract: Neural collapse (NC) describes the structured geometry that emerges in the features and weights of trained classifiers. Recent theory suggests NC can be suboptimal in deep architectures, attributing this to an explicit low-rank bias…

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