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Found 6 papers, 3 papers with code
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
We show that the model performs with high reliability and efficiency on the online CPD problem ($\sim$98\% and $\sim$97\% area under precision-recall curve respectively).
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation
Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties.
Learning Non-Unique Segmentation with Reward-Penalty Dice Loss
Semantic segmentation is one of the key problems in the field of computer vision, as it enables computer image understanding.
Segmented Pairwise Distance for Time Series with Large Discontinuities
Time series with large discontinuities are common in many scenarios.
Databases
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT.
