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Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation
Adversarial examples have shown that albeit highly accurate, models lear...
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Continual General Chunking Problem and SyncMap
Humans possess an inherent ability to chunk sequences into their constit...
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Evolving Robust Neural Architectures to Defend from Adversarial Attacks
Deep neural networks were shown to misclassify slightly modified input i...
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Uncovering Why Deep Neural Networks Lack Robustness: Representation Metrics that Link to Adversarial Attacks
Neural networks have been shown vulnerable to adversarial samples. Sligh...
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Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses
In adversarial machine learning, there are a huge number of attacks of v...
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Self Training Autonomous Driving Agent
Intrinsically, driving is a Markov Decision Process which suits well the...
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Batch Tournament Selection for Genetic Programming
Lexicase selection achieves very good solution quality by introducing or...
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Tackling Unit Commitment and Load Dispatch Problems Considering All Constraints with Evolutionary Computation
Unit commitment and load dispatch problems are important and complex pro...
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Understanding the One-Pixel Attack: Propagation Maps and Locality Analysis
Deep neural networks were shown to be vulnerable to single pixel modific...
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Universal Rules for Fooling Deep Neural Networks based Text Classification
Recently, deep learning based natural language processing techniques are...
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Spectrum-Diverse Neuroevolution with Unified Neural Models
Learning algorithms are being increasingly adopted in various applicatio...
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General Subpopulation Framework and Taming the Conflict Inside Populations
Structured evolutionary algorithms have been investigated for some time....
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Self Organizing Classifiers and Niched Fitness
Learning classifier systems are adaptive learning systems which have bee...
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Self Organizing Classifiers: First Steps in Structured Evolutionary Machine Learning
Learning classifier systems (LCSs) are evolutionary machine learning alg...
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Contingency Training
When applied to high-dimensional datasets, feature selection algorithms ...
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Novelty-organizing team of classifiers in noisy and dynamic environments
In the real world, the environment is constantly changing with the input...
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Attacking Convolutional Neural Network using Differential Evolution
The output of Convolutional Neural Networks (CNN) has been shown to be d...
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Lightweight Classification of IoT Malware based on Image Recognition
The Internet of Things (IoT) is an extension of the traditional Internet...
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One pixel attack for fooling deep neural networks
Recent research has revealed that the output of Deep Neural Networks (DN...
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