Phase Transitions in Neural Networks

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25 papers2 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. Attention over noisy examples: generalization and memorization in the exponential Hopfield network

    Oct 15, 2026Francesco AlemannoHopfield NetworksNeural Network Memorization

  2. Emergent phases of superposition: from partial to full representation

    Sep 29, 2026Lihao Guo, Yizhou Liu, Jeff GorePhase Transitions in Neural NetworksFeature Superposition

    Massachusetts Institute of Technology Cambridge, MA 02139, USA

  3. Learning-Induced Dynamical Transition in Recurrent Neural Networks

    Sep 16, 2026Varun VaidyaDynamical SystemsRecurrent Neural Networks

    Department of Physics, University of South Dakota, Vermillion 57069, USA

  4. Phases in a class of associative memories via hidden neurons

    Sep 10, 2026Toshihiro Ota, Masato TakiPhase TransitionsHopfield Networks

    CyberAgent AI Lab, Shibuya, Tokyo 150–0002, Japan · RIKEN iTHEMS, Wako, Saitama 351–0198, Japan · Graduate School of Artificial Intelligence and Science, Rikkyo University, Toshima, Tokyo 171–8501, Japan

  5. Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking

    Sep 9, 2026Anish KatariaNeural Network Training DynamicsNeural Scaling Laws

    Princeton University, Princeton, NJ, USA.

  6. Phase Transition Frequency as a Training Time Predictor of Test Accuracy in ResNets

    Sep 4, 2026Arunan JPhase Transitions in Neural NetworksNeural Network Training Dynamics

    Independent Researcher, Chennai, Tamil Nadu, 600080, India.

  7. Percolation Dynamics in Optimization: Variance Cascades and Nested Symmetry

    Sep 2, 2026Sai Niranjan Ramachandran, Suvrit SraPhase Transitions in Neural NetworksStochastic Gradient Descent

    School of Computation, Information and Technology, Technical University of Munich, Germany · Munich Center for Machine Learning (MCML)

  8. Dynamical phase selection controls compute scaling in looped transformers

    Aug 27, 2026Gunn KimCritical Slowing DownInference-Time Scaling

    Department of Physics, Sejong University, Seoul 05006, Republic of Korea

  9. Is Grokking a Loss of Normal Hyperbolicity of the Interpolation Manifold?

    Aug 14, 2026Suvinava BasakGrokkingNeural Network Training Dynamics

    Technische Universität Braunschweig, Data Science, Universitätsplatz 2, 38106 Braunschweig, Germany

  10. Broken Ergodicity and the Violation of the Fluctuation-Dissipation Theorem Lead to Generalization Beyond Overfitting in Machine Learning

    Jul 5, 2026Chan Li, Nigel GoldenfeldDouble DescentStatistical Physics of Learning

    Department of Physics, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. · Halıcıoğlu Data Science Institute, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA

  11. Can Machines Really See Objects in Images? A Study Based on Syntactic Distance and Visual Self-Referential Instances

    Jun 28, 2026Xingyu Peng, Junran Wu, Yue Hou +9Phase Transitions in Neural NetworksVisual Reasoning

    SKLCCSE, Beihang University · Xiaoyu Brain · Nanjing University +1

  12. Spectral phase transitions and trainability in neural network learning dynamics

    Jun 26, 2026Chanju Park, Dario Bocchi, Francesco D'Amico +2Random Matrix TheoryNeural Network Training Dynamics

    Centre for Quantum Fields and Gravity, Department of Physics, Swansea University, Swansea SA2 8PP, United Kingdom · Physics Department, University of Rome Sapienza, Piazzale Aldo Moro 5, 00185 Rome, Italy · Institute of Nanotechnology, CNR-NANOTEC, Piazzale Aldo Moro 5, 00185 Rome, Italy +1

  13. Noise-Driven Escape from Metastable Phases explains Grokking in Deep Neural Networks

    Jun 15, 2026Ibrahim Talha Ersoy, Karoline WiesnerStatistical Physics of LearningNeural Network Generalization

    Complexity Science Group, Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany

  14. Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence

    Jun 10, 2026Itay Lavie, Kirsten Fischer, Andrey Lekov +3Softmax AttentionPhase Transitions in Neural Networks

    Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904, Israel · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA · Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Jülich Research Center, Jülich 52428, Germany +3

  15. Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems

    May 25, 2026Truong Xuan KhanhDynamical SystemsPhase Transitions in Neural Networks

    H&K Research Studio, Clevix LLC, Hanoi, Vietnam

  16. Feature Lottery? A Bifurcation Theory of Concept Emergence

    May 22, 2026Fuming YangDynamical SystemsRepresentation Learning

    MIT

  17. Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry

    May 5, 2026Truong Xuan KhanhPhase TransitionsStatistical Learning Theory

    H&K Research Studio, Clevix LLC Hanoi, Vietnam

  18. Dimensional Criticality at Grokking Across MLPs and Transformers

    Apr 6, 2026Ping WangNeural Network Training DynamicsPhase Transitions in Neural Networks

    Institute of High Energy Physics, Chinese Academy of Science, 100049 Beijing, China

  19. Grokking as Dimensional Phase Transition in Neural Networks

    Apr 6, 2026Ping WangPhase Transitions in Neural NetworksGrokking

    Institute of High Energy Physics, Chinese Academy of Science, 100049 Beijing, China

  20. From Density Matrices to Phase Transitions in Deep Learning: Spectral Early Warnings and Interpretability

    Mar 31, 2026Max Hennick, Guillaume CorlouerPhase Transitions in Neural NetworksNeural Network Training Dynamics

  21. Spectral Edge Dynamics: An Analytical-Empirical Study of Phase Transitions in Neural Network Training

    Mar 30, 2026Yongzhong XuPhase Transitions in Neural NetworksNeural Network Training Dynamics

  22. Capability-Guided Compression: Toward Interpretability-Aware Budget Allocation for Large Language Models

    Mar 17, 2026Rishaank GuptaPhase Transitions in Neural NetworksModel Compression

    Independent Researcher

  23. Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia

    Mar 12, 2026Haining Pan, Nakul Aggarwal, J. H. PixleyPhase Transitions in Neural NetworksNeural Network Pruning

    Department of Physics and Astronomy, Center for Materials Theory, Rutgers University, Piscataway, NJ 08854 USA · Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA

  24. Marginals Before Conditionals

    Mar 10, 2026Mihir SahasrabudheNeural Network Training DynamicsPhase Transitions in Neural Networks

    School of Information Sciences, University of Illinois Urbana-Champaign

  25. Toward a Physics of Deep Learning and Brains

    Sep 26, 2025Arsham Ghavasieh, Meritxell Vila-Minana, Akanksha Khurd +3Phase Transitions in Neural NetworksStatistical Physics of Learning

    Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA · Department of Physics, Indiana University, Bloomington, Indiana 47405, USA · Institute for Advanced Study, Princeton, NJ 08540, USA