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| TimeGPT× | Vision Transformer× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2023 | 2021 |
| Twórca≠ | Fabio Garza | Dosovitskiy, A. et al. |
| Typ≠ | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Źródło pierwotne≠ | Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Inne nazwy≠ | TimeGPT-1, Time series GPT | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
| ScholarGateZbiór danych ↗ |
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