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Found 7 papers, 1 papers with code
evoML Yellow Paper: Evolutionary AI and Optimisation Studio
Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.
Real-time Detection of Practical Universal Adversarial Perturbations
Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attacks against Deep Neural Networks (DNNs).
Better Model Selection with a new Definition of Feature Importance
Feature importance aims at measuring how crucial each input feature is for model prediction.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification problems.
Cryptocurrency Trading: A Comprehensive Survey
This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e. g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others).
Ascertaining price formation in cryptocurrency markets with DeepLearning
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world.
Universal Adversarial Robustness of Texture and Shape-Biased Models
Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise.
