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Evolving Robust Neural Architectures to Defend from Adversarial Attacks
Shashank Kotyan, Danilo Vasconcellos Vargas
Neural networks are prone to misclassify slightly modified input images. Recently, many defences have been proposed, but none have improved the robustness of neural networks consis…
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences
Shashank Kotyan, Danilo Vasconcellos Vargas, Moe Matsuki
Neural networks have been shown vulnerable to a variety of adversarial algorithms. A crucial step to understanding the rationale for this lack of robustness is to assess the potent…
Adversarial Robustness Assessment: Why both and Attacks Are Necessary
Shashank Kotyan, Danilo Vasconcellos Vargas
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task.…
Self Training Autonomous Driving Agent
Shashank Kotyan, Danilo Vasconcellos Vargas, Venkanna U
Intrinsically, driving is a Markov Decision Process which suits well the reinforcement learning paradigm. In this paper, we propose a novel agent which learns to drive a vehicle wi…
Batch Tournament Selection for Genetic Programming
Vinicius V. Melo, Danilo Vasconcellos Vargas, Wolfgang Banzhaf
Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many a…
Tackling Unit Commitment and Load Dispatch Problems Considering All Constraints with Evolutionary Computation
Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano
Unit commitment and load dispatch problems are important and complex problems in power system operations that have being traditionally solved separately. In this paper, both proble…