GitHub topics: ecml-pkdd
thanapol2/ASTD_ECMLPKDD
[ECML-PKDD 2024 Research Track] Official source code of "Adaptive Seasonal-Trend Decomposition for Streaming Time Series Data with Transitions and Fluctuations in Seasonality"
Language: Jupyter Notebook - Size: 3.7 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0
developmentseed/chabud2023
Change detection for Burned area Delineation (ChaBuD) ECML/PKDD 2023 challenge
Language: Python - Size: 198 KB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 5 - Forks: 1
zanarashidi/AMoC
Adaptive Momentum Coefficient for Neural Network Optimization
Language: Python - Size: 200 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0
GU-DataLab/misinformation-detection-DeMis
Resource for misinformation research on Twitter. Official resource of the paper "DeMis: Data-efficient Misinformation Detection using Reinforcement Learning", ECML-PKDD 2022
Language: Python - Size: 13.7 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 5 - Forks: 1
Fraunhofer-AISEC/R2-AD2
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
Language: Python - Size: 42 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 0
Fraunhofer-AISEC/A3 📦
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
Language: Python - Size: 41 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 12 - Forks: 3