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Showing 1 to 4 of 4 for “"Regularized Models"”.

  1. Investigating the Capacity of Generative AI to Learn Genotype-by-Environment Interactions in Brachypodium distachyon

    … using both a baseline configuration and a set of regularized variants incorporating noise injection, co-expression preservation, entropy-based sample weighting, and masked gene modeling as a self-supervised objective. While baseline models achieved perfect reconstruction accuracy, they failed to …

    mit Repository record for Investigating the Capacity of Generative AI to Learn Genotype-by-Environment Interactions in Brachypodium distachyon (opens in a new tab)

  2. Geometric Functional Data Analysis

    … information requires the formulation of models able to incorporate structural information on the geometry of the underlying domains, with the aim of mitigating the curse of dimensionality. This is achieved by adopting regularized models involving differential operator penalties. Modeling …

    cambridge Repository record for Geometric Functional Data Analysis (opens in a new tab)

  3. Understanding Adversarial Robustness in Deep Learning

    … the adversarial robustness of deep learning models. Our investigation covers various aspects of this phenomenon, including the development of two new defense algorithms, two new attack algorithms, a novel definition of hierarchical adversarial robustness, and an analysis of how the …

    toronto-retro Repository record for Understanding Adversarial Robustness in Deep Learning (opens in a new tab)

  4. Stabilization of POD-ROMs

    … for stabilizing POD-ROMs (that is, reduced order models based on basis functions derived from the proper orthogonal decomposition) for both the CDR (convection-diffusion-reaction) equation and the NSEs (Navier-Stokes equations). Stabilization is necessary because standard POD-ROMs of …

    vt Repository record for Stabilization of POD-ROMs (opens in a new tab)