Summary of In-context In-context Learning with Transformer Neural Processes, by Matthew Ashman et al.
In-Context In-Context Learning with Transformer Neural Processes
by Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 introduces the Neural Processes (NPs) meta-learning model, which approximates the posterior predictive map of stochastic processes. NPs can be improved by integrating similar datasets into their architecture. The authors develop an in-context in-context learning pseudo-token TNP (ICICL-TNP), building on previous PT-TNPs that utilize transformer architectures to address quadratic computational complexity. ICICL-TNP enables conditioning on multiple datapoints and datasets, allowing for in-context in-context learning. Experiments demonstrate the importance of this paradigm and the effectiveness of ICICL-TNP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way for computers to learn from data that’s similar but not exactly the same. It’s like having multiple puzzles with some pieces being the same, and the computer can use that information to solve each puzzle better. The scientists created a new model called ICICL-TNP that can do this, which helps it make more accurate predictions. They tested this idea and showed that it works well in different situations. |
Keywords
» Artificial intelligence » Meta learning » Token » Transformer
