J
James McDermott
Researcher at National University of Ireland, Galway
Publications - 86
Citations - 2277
James McDermott is an academic researcher from National University of Ireland, Galway. The author has contributed to research in topics: Genetic programming & Grammatical evolution. The author has an hindex of 25, co-authored 83 publications receiving 1876 citations. Previous affiliations of James McDermott include Massachusetts Institute of Technology & University College Dublin.
Papers
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Proceedings ArticleDOI
Genetic programming needs better benchmarks
James McDermott,David White,Sean Luke,Luca Manzoni,Mauro Castelli,Leonardo Vanneschi,Wojciech Jaskowski,Krzysztof Krawiec,Robin Harper,Kenneth de Jong,Una-May O'Reilly +10 more
TL;DR: This paper argues that the definition of standard benchmarks is an essential step in the maturation of the field and motivates the development of a benchmark suite and defines its goals.
Journal ArticleDOI
Better GP benchmarks: community survey results and proposals
David White,James McDermott,Mauro Castelli,Luca Manzoni,Brian W. Goldman,Gabriel Kronberger,Wojciech Jaśkowski,Una-May O'Reilly,Sean Luke +8 more
TL;DR: Community support is found for creating a “blacklist” of problems which are in common use but have important flaws, and whose use should therefore be discouraged, and a set of possible replacement problems are proposed.
Posted Content
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network
TL;DR: In this paper, a real-time collective anomaly detection model based on neural network learning and feature operating is proposed, where a LSTM RNN is trained with normal time series data before performing a live prediction for each time step.
Book ChapterDOI
Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks
TL;DR: This paper proposes a real time collective anomaly detection model based on neural network learning that is built on a time series version of the KDD 1999 dataset and demonstrates that it is possible to offer reliable and efficient collective anomalies detection.
Journal ArticleDOI
Learning Neural Representations for Network Anomaly Detection
TL;DR: The proposed latent representation models help classifiers to perform efficiently and consistently on high-dimensional and sparse network datasets, even with relatively few training points, and can minimize the effect of model selection on these classifiers since their performance is insensitive to a wide range of hyperparameter settings.