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Found 9 papers, 4 papers with code
Deep Neural Networks via Complex Network Theory: a Perspective
For the novel metrics, in addition to the existing ones, we provide a mathematical formalisation for Fully Connected, AutoEncoder, Convolutional and Recurrent neural networks, of which we vary the activation functions and the number of hidden layers.
Fragility, Robustness and Antifragility in Deep Learning
We define three \textit{filtering scores} for quantifying the fragility, robustness and antifragility characteristics of DNN parameters based on the performances for (i) clean dataset, (ii) adversarial dataset, and (iii) the difference in performances of clean and adversarial datasets.
Adaptive search space decomposition method for pre- and post- buckling analyses of space truss structures
The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures.
Deep Neural Networks as Complex Networks
Deep Neural Networks are, from a physical perspective, graphs whose `links` and `vertices` iteratively process data and solve tasks sub-optimally.
Assessing Ranking and Effectiveness of Evolutionary Algorithm Hyperparameters Using Global Sensitivity Analysis Methodologies
We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem.
Backpropagation Neural Tree
We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree.
Ranked #1000000000 on
Image Classification
on MNIST
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons
In this paper, we evaluate the robustness of state-of-the-art image classification models trained on the MNIST and CIFAR10 datasets against the fast gradient sign method attack, a simple yet effective method of deceiving neural networks.
Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks
In this paper, we interpret Deep Neural Networks with Complex Network Theory.
Multi-Objective Optimisation of Multi-Output Neural Trees
We propose an algorithm and a new method to tackle the classification problems.
Ranked #1 on
General Classification
on iris
