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Found 8 papers, 1 papers with code
MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
In this paper, we propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach that generalizes the idea of motion imitation for one character to deal with both the interaction and the motions of the multiple physics-based characters.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data.
Temporal Disaggregation of the Cumulative Grass Growth
To address this problem, our method applies time series forecasting using climate information and grass growth from previous time steps.
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation.
Day-ahead time series forecasting: application to capacity planning
In the context of capacity planning, forecasting the evolution of informatics servers usage enables companies to better manage their computational resources.
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex.
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification
In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words.
Event and Anomaly Detection Using Tucker3 Decomposition
Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the anomaly detection literature and can be applied to matrix data (e. g. Users-Features).
