The Numerical Intelligent Systems Laboratory focuses on performing cutting-edge research into fundamental problems in artificial intelligence and machine learning from a numerical computation perspective. We are exploring problems in advanced knowledge representation, inference, and learning as it applies to system-level problems such as system monitoring and control, equipment health management, and precision agriculture. Techniques explored include probabilistic and Bayesian methods, evolutionary methods, and particle-based methods. We are also exploring problems in deep learning and explainable AI.
Numerical Intelligent Systems Laboratory
Popular repositories
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HSI-BandSelection
HSI-BandSelection PublicDeveloping Low-Cost Multispectral Imagers using Inter-Band Redundancy Analysis and Greedy Spectral Selection in Hyperspectral Imaging.
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PredictionIntervals
PredictionIntervals PublicDualAQD: Dual Accuracy-quality-driven Prediction Intervals
Jupyter Notebook 6
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ResponsivityAnalysis
ResponsivityAnalysis PublicCounterfactual explanations for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes
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MultiSetSR
MultiSetSR PublicUnivariate Skeleton Prediction in Multivariate Systems Using Transformers
Python
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ManagementZonesCFE
ManagementZonesCFE PublicCounterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones
Python
Repositories
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- HSI-BandSelection Public
Developing Low-Cost Multispectral Imagers using Inter-Band Redundancy Analysis and Greedy Spectral Selection in Hyperspectral Imaging.
- ManagementZonesCFE Public
Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones
- ResponsivityAnalysis Public
Counterfactual explanations for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes
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