mlsanityData to 7 Oct 2026

Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework

arXiv 2503.17072 ↗First posted 21 Mar 2025cs.LG cs.NIcited by 2

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Abstract (arXiv)

We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 21 Mar 2025.

Builds on

4 of 14 references.

References · 4

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Improving Generalization Performance by Switching from Adam to SGDcited by 121
  3. 2022Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoderscited by 12
  4. 2023Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gaincited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Agentic AI for Scalable and Robust Optical Systems Controlcited by 2
  2. 2025Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrumcited by 0