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Research paper analyzes epistemic uncertainty in overparametrized neural networks

By PulseAugur Editorial · [2 sources] ·

A new research paper explores epistemic uncertainty in overparametrized neural networks, challenging the traditional view that this uncertainty diminishes with more data. The study highlights that non-identifiable model parameters, common in such networks due to symmetries, can lead to persistent uncertainty even when the underlying function is fully understood. The research analyzes discrete and continuous sources of this residual uncertainty, focusing on one-hidden-layer ReLU networks and validating theoretical findings with empirical studies. AI

IMPACT This research could refine our understanding of model limitations and inform the development of more robust neural network architectures.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Research paper analyzes epistemic uncertainty in overparametrized neural networks

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · David R\"ugamer ·

    On the Epistemic Uncertainty of Overparametrized Neural Networks

    arXiv:2605.25234v1 Announce Type: cross Abstract: Epistemic uncertainty is often viewed as a reducible uncertainty that vanishes with increasing data. This perspective implicitly assumes parameter identifiability and equates epistemic uncertainty with predictive variability. In o…

  2. arXiv stat.ML TIER_1 English(EN) · David Rügamer ·

    On the Epistemic Uncertainty of Overparametrized Neural Networks

    Epistemic uncertainty is often viewed as a reducible uncertainty that vanishes with increasing data. This perspective implicitly assumes parameter identifiability and equates epistemic uncertainty with predictive variability. In overparametrized neural networks, however, model pa…

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