paper:doi-10-1038-nature14539Deep learning
Related work— refs + corpus + external arXiv
Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
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- There Will Be a Scientific Theory of Deep LearningDaniel Kunin, Alexander Atanasov, Enric Boix-Adser\`a, Blake Bordelon, Jeremy Cohen, Nikhil Ghosh, Florentin Guth, Arthur Jacot, Mason Kamb, Dhruva Karkada, Eric J. Michaud, Berkan Ottlik, Joseph Turnbull Jamie Simon2026≈ 75%
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- ≈ 72%
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- A Pattern Language for Machine Learning TasksIan Fan, Tuomas Laakkonen, Neil John Ortega, Thomas Hoffmann, Vincent Wang-Mascianica Benjamin Rodatz2025≈ 70%
- ≈ 69%
- Discrete Latent Structure in Neural NetworksCaio F. Corro, Nikita Nangia, Tsvetomila Mihaylova, Andr\'e F. T. Martins Vlad Niculae2026≈ 69%
- ≈ 69%
- Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery SystemsMattia Cerrato, Jannis Brugger, Sa\v{s}o D\v{z}eroski, Ross King Stefan Kramer2026≈ 69%
- Learning without neurons in physical systemsin corpus2022≈ 68%
- ≈ 65%
- The Platonic Representation Hypothesisin corpus2024≈ 64%
- ≈ 63%
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- The World Inside Neural Networksin corpus2026≈ 63%
- Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representationsin corpus2023≈ 63%
- Model Alignment Searchin corpus2025≈ 63%
- Collective intelligence: A unifying concept for integrating biology across scales and substratesin corpus2024≈ 62%
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- Why Learning Requires Feelingin corpus2026≈ 62%
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