Blar i AURA på forfatter "Goodwin, Morten"
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Machine Learning Applications for Load Predictions in Electrical Energy Network
Johannesen, Nils Jacob (Doctoral Dissertations at the University of Agder; no. 376, Doctoral thesis, 2022)In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques ... -
MapAI: Precision in BuildingSegmentation
Jyhne, Sander; Goodwin, Morten; Andersen, Per-Arne; Oveland, Ivar; Nossum, Alexander Salveson; Ormseth, Karianne Øydegard; Ørstavik, Mathilde; Flatman, Andrew C. (Peer reviewed; Journal article, 2022)MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) 1 in collaboration with Centre for Artificial Intelligence Research at the University ... -
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
Abeyrathna, Kuruge Darshana; Bhattarai, Bimal; Goodwin, Morten; Gorji, Saeed Rahimi; Granmo, Ole-Christoffer; Lei, Jiao; Saha, Rupsa; Yadav, Rohan Kumar (Peer reviewed; Journal article, 2021)Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes ... -
Mastering DeepRTS with Transformers
Eike, Andreas Høiberg; Alves, Pedro (Master thesis, 2023)The Transformer deep learning model has recently proven its superiority in tasks like natural language processing and computer vision, as tools like ChatGPT and DALL-E have become widespread and helps humans complete tasks ... -
Mastering DeepRTS with Transformers
Eike, Andreas Høiberg; Alves, Pedro (Master thesis, 2023)The Transformer deep learning model has recently proven its superiority in tasks like natural language processing and computer vision, as tools like ChatGPT and DALL-E have become widespread and helps humans complete tasks ... -
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
Sharma, Jivitesh; Giri, Charul; Granmo, Ole-Christoffer; Goodwin, Morten (Journal article; Peer reviewed, 2019)Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works ... -
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
Sharma, Jivitesh; Giri, Charul; Granmo, Ole-Christoffer; Goodwin, Morten (Peer reviewed; Journal article, 2019)Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works ... -
A multi-step finite-state automaton for arbitrarily deterministic Tsetlin Machine learning
Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Shafik, Rishad; Lei, Jiao; Wheeldon, Adrian; Yakovlev, Alex; Lei, Jie; Goodwin, Morten (Peer reviewed; Journal article, 2021) -
Neuroevolution of Actively Controlled Virtual Characters - An Experiment for an Eight-Legged Character
Albrigstsen, Svein Inge; Imenes, Alexander; Goodwin, Morten; Lei, Jiao; Nunavath, Vimala (Chapter; Peer reviewed, 2018) -
On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth
Yazidi, Anis; Oommen, John; Goodwin, Morten (Chapter; Peer reviewed, 2015) -
On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments
Yazidi, Anis; Oommen, John; Goodwin, Morten (Journal article; Peer reviewed, 2016)The purpose of this paper is to propose a solution to an extremely pertinent problem, namely, that of identifying unreliable sensors (in a domain of reliable and unreliable ones) without any knowledge of the ground truth. ... -
PolyACO+: a multi-level polygon-based ant colony optimisation classifier
Goodwin, Morten; Tufteland, Torry; Ødesneltvedt, Guro; Yazidi, Anis (Journal article; Peer reviewed, 2017)Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt ... -
Positionless aspect based sentiment analysis using attention mechanism.
Yadav, Rohan Kumar; Lei, Jiao; Goodwin, Morten; Granmo, Ole-Christoffer (Peer reviewed; Journal article, 2021) -
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Zhang, Xuan; Lei, Jiao; Goodwin, Morten (Peer reviewed; Journal article, 2019) -
Relative evaluation of regression tools for urban area electrical energy demand forecasting
Johannesen, Nils Jakob; Kolhe, Mohan Lal; Goodwin, Morten (Journal article; Peer reviewed, 2019)Load forecasting is the most fundamental application in Smart-Grid, which provides essential input to Demand Response, Topology Optimization and Abnormally Detection, facilitating the integration of intermittent clean ... -
Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation
Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer; Goodwin, Morten (IJCAI International Joint Conference on Artificial Intelligence, Peer reviewed; Conference object, 2022)The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution ... -
SleepXAI: An explainable deep learning approach for multi-class sleep stage identification
Dutt, Micheal; Redhu, Surender; Goodwin, Morten; Omlin, Christian Walter Peter (Peer reviewed; Journal article, 2022)Extensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive ... -
Source Code Auto Completion with different approaches: Text Classification, N-Gram Models, Long Short-Term Memory
Ibrahim, Pavel (Master thesis, 2022)Et gjennomgang av 2 ulike Source Code Auto Completion metoder med Text Classification og N-Gram Modeller. I tillegg et forsøk på implementasjon av Long Short-Term Memory for Auto Completion med Python. -
Temperate fish detection and classification: a deep learning based approach
Knausgård, Kristian Muri; Wiklund, Arne; Sørdalen, Tonje Knutsen; Halvorsen, Kim Aleksander Tallaksen; Kleiven, Alf Ring; Lei, Jiao; Goodwin, Morten (Peer reviewed; Journal article, 2021)A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize ... -
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Andersen, Per-Arne; Goodwin, Morten; Granmo, Ole-Christoffer (Journal article; Peer reviewed, 2018)Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement ...