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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.05168 |
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| _version_ | 1866916197532958720 |
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| author | Foong, Tham Yik Zhang, Heng Yuan, Mao Po Vargas, Danilo Vasconcellos |
| author_facet | Foong, Tham Yik Zhang, Heng Yuan, Mao Po Vargas, Danilo Vasconcellos |
| contents | Input distribution shift presents a significant problem in many real-world systems. Here we present Xenovert, an adaptive algorithm that can dynamically adapt to changes in input distribution. It is a perfect binary tree that adaptively divides a continuous input space into several intervals of uniform density while receiving a continuous stream of input. This process indirectly maps the source distribution to the shifted target distribution, preserving the data's relationship with the downstream decoder/operation, even after the shift occurs. In this paper, we demonstrated how a neural network integrated with Xenovert achieved better results in 4 out of 5 shifted datasets, saving the hurdle of retraining a machine learning model. We anticipate that Xenovert can be applied to many more applications that require adaptation to unforeseen input distribution shifts, even when the distribution shift is drastic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations Foong, Tham Yik Zhang, Heng Yuan, Mao Po Vargas, Danilo Vasconcellos Machine Learning Input distribution shift presents a significant problem in many real-world systems. Here we present Xenovert, an adaptive algorithm that can dynamically adapt to changes in input distribution. It is a perfect binary tree that adaptively divides a continuous input space into several intervals of uniform density while receiving a continuous stream of input. This process indirectly maps the source distribution to the shifted target distribution, preserving the data's relationship with the downstream decoder/operation, even after the shift occurs. In this paper, we demonstrated how a neural network integrated with Xenovert achieved better results in 4 out of 5 shifted datasets, saving the hurdle of retraining a machine learning model. We anticipate that Xenovert can be applied to many more applications that require adaptation to unforeseen input distribution shifts, even when the distribution shift is drastic. |
| title | Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.05168 |