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  1. Jul 21, 2023Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are however difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic ...
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  3. pubmed.ncbi.nlm.nih.gov

    Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. ... Learning minimal representations of stochastic processes with variational autoencoders Phys Rev E. 2024 Jul;110(1):L012102. doi: 10.1103/PhysRevE.110.L012102. Authors Gabriel ...
  4. Learning minimal representations of stochastic processes with variational autoencoders Gabriel Fern´andez-Fernandez,1 Carlo Manzo,2,3 Maciej Lewenstein,1,4 Alexandre Dauphin,1 and Gorka Munoz-Gil˜5 1ICFO - Institut de Ci`encies Fot`oniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain
  5. paperswithcode.com

    Jul 21, 2023Due to their intrinsic randomness and uncertainty, they are however difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method builds upon an extended $\beta$-variational autoencoder architecture.
  6. researchgate.net

    Jul 21, 2023Download Citation | Learning minimal representations of stochastic processes with variational autoencoders | Stochastic processes have found numerous applications in science, as they are broadly ...
  7. semanticscholar.org

    TABLE I. Architecture layers' details. We use ReLU as non-linear activation function in all layers, kernel of size three and stride one on the convolutional (Conv.) layers, and no padding. We abbreviate the terms: batch size B, dilation dc, number of convolutional channels Nc, and receptive field RF. - "Learning minimal representations of stochastic processes with variational autoencoders"
  8. journals.aps.org

    Jul 18, 2024Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are, however, difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic ...
  9. mon.uvic.cat

    Oct 21, 2024Fernández-Fernández, G., Manzo, C., Lewenstein, M., Dauphin, A., & Muñoz-Gil, G. (2024). Learning minimal representations of stochastic processes with variational ...
  10. Bibliographic details on Learning minimal representations of stochastic processes with variational autoencoders. Stop the war! Остановите войну! solidarity - ... Learning minimal representations of stochastic processes with variational autoencoders. CoRR abs/2307.11608 (2023) a service of . home. blog; statistics; update feed;

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