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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Adversarially Robust Learning via Entropic Regularization.

Gauri Jagatap1, Ameya Joshi1, Animesh Basak Chowdhury1

  • 1Electrical and Computer Engineering, New York University, New York, NY, United States.

Frontiers in Artificial Intelligence
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

We introduce ATENT, a new algorithm family for training robust deep neural networks. ATENT uses a novel entropic regularization loss function to improve adversarial robustness and classification accuracy.

Keywords:
adversarial attackadversarial learningneural network trainingregularizationrobustness

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Area of Science:

  • Machine Learning
  • Deep Neural Networks
  • Adversarial Robustness

Background:

  • Deep neural networks are vulnerable to adversarial attacks, compromising their reliability.
  • Existing methods for adversarial robustness often face limitations in performance or scalability.

Purpose of the Study:

  • To propose a novel family of algorithms, ATENT, for enhancing adversarial robustness in deep neural networks.
  • To develop a new loss function incorporating entropic regularization for improved training stability and robustness.

Main Methods:

  • Formulation of a new loss function with entropic regularization.
  • Generation of adversarial samples from a specialized distribution emphasizing high-loss regions near training data.
  • Optimization of the proposed loss function to identify robust valleys in the loss landscape.

Main Results:

  • The ATENT algorithms demonstrate competitive or superior robust classification accuracy compared to state-of-the-art methods.
  • Effective performance on benchmark datasets like MNIST and CIFAR-10, validating the approach.

Conclusions:

  • The proposed ATENT algorithms offer a promising direction for training adversarially robust deep neural networks.
  • The novel entropic regularization loss function effectively improves robustness against adversarial perturbations.