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Found 4 papers, 2 papers with code
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization
We achieve this by introducing novel state space representations for the base models, which are then combined to provide a full state space representation of the hybrid or the ensemble.
Context-Aware Ensemble Learning for Time Series
Unlike all the literature in ensembling, for the first time, we introduce a new approach using a meta learner that effectively combines the base model predictions via using a superset of the features that is the union of the base models' feature vectors instead of the predictions themselves.
Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms
To this end, we introduce a novel deep learning architecture for forecasting high-resolution spatio-temporal weather data.
Semi-Automatic Annotation For Visual Object Tracking
For detection, we use an off-the-shelf object detector which is trained iteratively with the annotations generated by the proposed method, and we perform object detection on each frame independently.
