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7 papers
Mantis: Lightweight Foundation Model for Time Series Classification
Vasilii Feofanov, Songkang Wen, Shifeng Xie +10
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly f…
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
Shifeng Xie, Vasilii Feofanov, Ambroise Odonnat +7
Time series foundation models (TSFMs) have recently gained significant attention due to their strong zero-shot capabilities and widespread real-world applications. Such models typi…
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
Vasilii Feofanov, Songkang Wen, Jianfeng Zhang +2
Developing foundation models for time series classification is of high practical relevance, as such models can serve as universal feature extractors for diverse downstream tasks. A…
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov +3
Estimating the test performance of a model, possibly under distribution shift, without having access to the ground-truth labels is a challenging, yet very important problem for the…
IGN : Implicit Generative Networks
Haozheng Luo, Tianyi Wu, Colin Feiyu Han +1
In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by usin…
Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
Songkang Wen, Vasilii Feofanov, Jianfeng Zhang
Recently, there has been a growing interest in time series foundation models that generalize across different downstream tasks. A key to strong foundation models is a diverse pre-t…