mlsanityData to 14 Oct 2026

Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search

arXiv 2506.21772 ↗First posted 11 Jun 2025eess.SP cs.LGcited by 0

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Abstract (arXiv)

Recent research works establish deep neural networks as high performing tools for radar target detection, especially on challenging environments (presence of clutter or interferences, multi-target scenarii...). However, the usually large computational complexity of these networks is one of the factors preventing them from being widely implemented in embedded radar systems. We propose to investigate novel neural architecture search (NAS) methods, based on Monte-Carlo Tree Search (MCTS), for finding neural networks achieving the required detection performance and striving towards a lower computational complexity. We evaluate the searched architectures on endoclutter radar signals, in order to compare their respective performance metrics and generalization properties. A novel network satisfying the required detection probability while being significantly lighter than the expert-designed baseline is proposed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 11 Jun 2025.

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16 of 32 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  4. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  5. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  6. 2020Pruning neural networks without any data by iteratively conserving synaptic flowcited by 479
  7. 2020Picking Winning Tickets Before Training by Preserving Gradient Flowcited by 471
  8. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 414
  9. 2020Neural Architecture Search without Trainingcited by 244
  10. 2021Zero-Cost Proxies for Lightweight NAScited by 199
  11. 2019AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Searchcited by 33
  12. 2017DeepArchitect: Automatically Designing and Training Deep Architecturescited by 28
4 more
  1. 2021NASI: Label- and Data-agnostic Neural Architecture Search at Initializationcited by 28
  2. 2019Deep Radar Detectorcited by 22
  3. 2022DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classificationcited by 7
  4. 2017Finding Competitive Network Architectures Within a Day Using UCTcited by 3

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