mlsanityData to 14 Oct 2026

Who invented deep residual learning?

arXiv 2509.24732 ↗First posted 29 Sep 2025cs.LG cs.NEcited by 0

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Abstract (arXiv)

Modern AI is based on deep artificial neural networks (NNs). As of 2025, the most cited scientific article of the 21st century is an NN paper on deep residual learning with residual connections. Who invented this? We present a timeline of the evolution of deep residual learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 29 Sep 2025.

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14 of 49 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  4. 2021Linear Transformers Are Secretly Fast Weight Programmerscited by 502
  5. 2018Efficient Attention: Attention with Linear Complexitiescited by 368
  6. 2021Random Feature Attentioncited by 354
  7. 2014Hypercolumns for Object Segmentation and Fine-grained Localizationcited by 128
  8. 2021Going Beyond Linear Transformers with Recurrent Fast Weight Programmerscited by 89
  9. 2018The Importance of Being Recurrent for Modeling Hierarchical Structurecited by 42
  10. 2018Learning to Reason with Third-Order Tensor Productscited by 27
  11. 2022Annotated History of Modern AI and Deep Learningcited by 20
  12. 2023Exploring the Promise and Limits of Real-Time Recurrent Learningcited by 19
2 more
  1. 2022Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rulescited by 16
  2. 2022Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rulescited by 9

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