Summary of Seq-to-final: a Benchmark For Tuning From Sequential Distributions to a Final Time Point, by Christina X Ji et al.
Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point
by Christina X Ji, Ahmed M Alaa, David Sontag
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the problem of distribution shift over time, where limited data is available in the final period. Few methods have been developed specifically for this purpose. The authors construct a benchmark called Seq-to-Final to evaluate three classes of methods: those that learn from all data without adapting to the final period, those that learn from historical data with no regard to the sequential nature and then adapt to the final period, and those that leverage the sequential nature of historical data when tailoring a model to the final period. The benchmark focuses on image classification tasks using CIFAR-10 and CIFAR-100 as the base images for synthetic sequences. The authors also evaluate the same methods on the Portraits dataset to explore the relevance to real-world shifts over time. The results suggest that methods that disregard the sequential structure and adapt to the final time point tend to perform well, while approaches that leverage the sequential nature do not offer any improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to learn a model when you only have limited data at the end. This problem happens often in real life, like when you’re trying to predict what will happen next year based on data from previous years. The authors created a special test to see which methods work best for this problem. They tested three types of methods: those that learn from all the data without thinking about the future, those that learn from the past but don’t care about the order of events, and those that think about the order of events when learning. They used pictures as examples and found that the first type of method worked best. |
Keywords
» Artificial intelligence » Image classification
