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Mark Mühlau

2 accepted papers

2026

Beyond Uniformity: Regularizing Implicit Neural Representations through a Lipschitz Lens

ICLR 2026poster

Implicit Neural Representations (INRs) have shown great promise in solving inverse problems, but their lack of inherent regularization often leads to a trade-off between expressiveness and smoothness. While Lipschitz continuity presents a principled form of implicit regularization, it is often appli…

Cited by 0SourceScholar
2026

Optimizing Rank for High-Fidelity Implicit Neural Representations

ICML 2026poster

Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed research efforts towards architectural interventions, such as coordinate embeddings or specialized activation functions…

Cited by 0SourceScholar