Summary of Neuralsolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks, by Bernardo Esteves et al.
NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks
by Bernardo Esteves, Miguel Vasco, Francisco S. Melo
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 NeuralSolver is a recurrent solver that can efficiently learn algorithms from smaller problems and apply them to larger ones. Unlike previous recurrent solvers, NeuralSolver can be used in both same-size and different-size problems. To achieve this versatility, it consists of three main components: a recurrent module, a processing module, and a curriculum-based training scheme. The method is evaluated on novel different-size tasks and outperforms prior state-of-the-art recurrent solvers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuralSolver is a new way to use computers to solve problems. It can learn from smaller problems and then use that knowledge to solve bigger ones. This is special because most computer programs can only do the same size problem. NeuralSolver has three parts: one that remembers things, one that brings it all together, and one that helps it get better at learning. People tested NeuralSolver on new kinds of problems and it worked better than other similar programs. |
