Summary of Dncs Require More Planning Steps, by Yara Shamshoum et al.
DNCs Require More Planning Steps
by Yara Shamshoum, Nitzan Hodos, Yuval Sieradzki, Assaf Schuster
First submitted to arxiv on: 4 Jun 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 investigates the impact of computational constraints on the generalization abilities of machine learning models. Specifically, it examines how limitations on planning steps (planning budget) and external memory usage affect the performance of implicit algorithmic solvers like the Differentiable Neural Computer (DNC). The authors demonstrate that varying the planning budget can significantly alter the learned time complexity, training time, stability, and generalization capabilities of the DNC model. They evaluate their method on four benchmark tasks: Graph Shortest Path, Convex Hull, Graph MinCut, and Associative Recall. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models can solve complex problems. Right now, these models don’t think about how much time or memory they need to get the job done. This can make them not very good at solving certain problems. The authors want to know what happens if we give a model a limit on how many steps it can take to find a solution (planning budget). They use a special type of machine learning model called the Differentiable Neural Computer (DNC) and test their idea on four different tasks. They found that changing the planning budget can make the model better or worse at solving problems. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Recall
