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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Probabilistic robustness estimates for feed-forward neural networks.

Nicolas Couellan¹

¹ENAC, Université de Toulouse, 7 Avenue Edouard Belin, 31400 Toulouse, France; Institut de Mathématiques de Toulouse, Université de Toulouse, UPS IMT, F-31062 Toulouse Cedex 9, France.

Neural Networks : the Official Journal of the International Neural Network Society

|May 17, 2021

Summary

We developed a method to estimate the robustness of deep neural networks (DNNs) against noise. Our approach accurately predicts how much a DNN

Keywords:

Deep neural network Neural network robustness Random noise attacks Regularization

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Machine Learning
Deep Learning
Artificial Intelligence

Background:

Deep neural networks (DNNs) are widely used but vulnerable to adversarial attacks.
Ensuring the robustness of DNNs is crucial for reliable real-world applications.
Quantifying DNN output deviation under input perturbations is a key challenge.

Purpose of the Study:

To propose a method for estimating the probability of output deviation in feed-forward neural networks under random noise attacks.
To provide a theoretical framework for understanding and improving DNN robustness.
To develop a regularization technique for enhancing DNN training.

Main Methods:

Derivation of a concentration inequality for input uncertainty propagation using the Cramer-Chernoff method.
Estimation of local variation of the neural network mapping at training points.
Development of a loss function regularization based on the derived network condition.

Main Results:

A simple concentration inequality was derived to bound the deviation of DNN outputs.
The proposed method effectively regularizes the loss function during training.
Empirical assessment on public datasets confirmed accurate estimation of DNN robustness.

Conclusions:

The proposed Cramer-Chernoff based method provides a reliable way to estimate DNN robustness against noise.
The derived theoretical insights can be directly applied to improve DNN training and reliability.
This work contributes to building more secure and trustworthy deep learning systems.