Holistic Deep Learning

• URL: http://arxiv.org/abs/2110.15829v5
• Date: Mon, 20 Mar 2023 23:39:56 GMT
• Title: Holistic Deep Learning
• Authors: Dimitris Bertsimas, Kimberly Villalobos Carballo, L\'eonard Boussioux, Michael Lingzhi Li, Alex Paskov, Ivan Paskov
• Abstract summary: This paper presents a novel holistic deep learning framework that addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models.
• Score: 3.718942345103135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL.

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