IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v608y2022ip1s0378437122008342.html
   My bibliography  Save this article

An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning

Author

Listed:
  • Shukla, Mohak
  • Thakur, Ajay D.

Abstract

Physicists have had a keen interest in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for a while, with a special inclination towards unravelling the fundamental mechanism behind the process of learning. In particular, exploring the underlying mathematical structure of a neural net (NN) is expected to not only help us understand the epistemological meaning of ‘Learning’ but also has the potential to unravel the secrets behind the workings of the brain. Here, it is worthwhile to establish correspondences and draw parallels between methods developed in core areas of Physics and the techniques developed at the forefront of AI and ML. Although recent explorations indicating a mapping between the Renormalization Group (RG) and Deep Learning (DL) have shown valuable insights, we intend to investigate the relationship between RG and Autoencoders (AE) in particular. We use Transfer Learning (TL) to embed the coarse-graining procedure in a NN and compare it with the underlying mechanism of encoding–decoding through a series of tests.

Suggested Citation

  • Shukla, Mohak & Thakur, Ajay D., 2022. "An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008342
    DOI: 10.1016/j.physa.2022.128276
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122008342
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128276?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tan Wang & L. Jeff Hong, 2023. "Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 196-215, January.
    2. Léon Faure & Bastien Mollet & Wolfram Liebermeister & Jean-Loup Faulon, 2023. "A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    4. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    5. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    6. Kiran Krishnamachari & Dylan Lu & Alexander Swift-Scott & Anuar Yeraliyev & Kayla Lee & Weitai Huang & Sim Ngak Leng & Anders Jacobsen Skanderup, 2022. "Accurate somatic variant detection using weakly supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    7. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Jackie Grant & Mark Hindmarsh & Sergey E. Koposov, 2022. "The distribution of loss to future USS pensions due to the UUK cuts of April 2022," Papers 2206.06201, arXiv.org.
    10. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    11. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    12. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    13. Laura Portell & Sergi Morera & Helena Ramalhinho, 2022. "Door-to-Door Transportation Services for Reduced Mobility Population: A Descriptive Analytics of the City of Barcelona," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    14. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    15. Jonas Bunsen & Matthias Finkbeiner, 2022. "An Introductory Review of Input-Output Analysis in Sustainability Sciences Including Potential Implications of Aggregation," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    16. Petros C. Lazaridis & Ioannis E. Kavvadias & Konstantinos Demertzis & Lazaros Iliadis & Lazaros K. Vasiliadis, 2023. "Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences," Sustainability, MDPI, vol. 15(17), pages 1-31, August.
    17. Matthias Wagener & Andriette Bekker & Mohammad Arashi, 2021. "Mastering the Body and Tail Shape of a Distribution," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    18. Gallo Cassarino, Tiziano & Barrett, Mark, 2022. "Meeting UK heat demands in zero emission renewable energy systems using storage and interconnectors," Applied Energy, Elsevier, vol. 306(PB).
    19. Akshay J. Maheshwari & Jonathan Calles & Sean K. Waterton & Drew Endy, 2023. "Engineering tRNA abundances for synthetic cellular systems," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    20. Maren Schnieder, 2023. "Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests," Sustainability, MDPI, vol. 15(18), pages 1-15, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.