Summary of On Logical Extrapolation For Mazes with Recurrent and Implicit Networks, by Brandon Knutson and Amandin Chyba Rabeendran and Michael Ivanitskiy and Jordan Pettyjohn and Cecilia Diniz-behn and Samy Wu Fung and Daniel Mckenzie
On Logical Extrapolation for Mazes with Recurrent and Implicit Networks
by Brandon Knutson, Amandin Chyba Rabeendran, Michael Ivanitskiy, Jordan Pettyjohn, Cecilia Diniz-Behn, Samy Wu Fung, Daniel McKenzie
First submitted to arxiv on: 3 Oct 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 Recent research has explored the capabilities of neural network architectures, specifically recurrent neural networks (RNNs) and implicit neural networks (INNs), in performing logical extrapolation. This involves training a model on simple instances of a task and applying it to more challenging situations. Our paper revisits this idea and finds that the capacity for extrapolation is less robust than previously suggested. We demonstrate that INNs can generalize to larger maze instances, but struggle to generalize along other axes of difficulty. Additionally, we show that models explicitly trained to converge to a fixed point tend to do so when extrapolating, whereas non-converging models may exhibit limit cycles or correct problem-solving despite the unconventional behavior. Our results highlight the need for further investigation into why these networks extrapolate easily in certain directions but struggle with others, and how analyzing extrapolation dynamics can inform the design of more efficient and interpretable logical extrapolators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Some neural networks are thought to be able to solve problems they haven’t seen before. This is called logical extrapolation. In this study, we looked at two types of neural networks that have been shown to do this: recurrent neural networks (RNNs) and implicit neural networks (INNs). We found out that these networks are only good at solving new problems if the problem is just a bigger version of one they’ve seen before. They struggle with problems that are similar but harder or easier in different ways. We also discovered that some networks will always try to solve the problem in the same way, while others might find different solutions. Overall, our study shows that these networks have limitations and that we need to keep studying them to understand why they work the way they do. |
Keywords
» Artificial intelligence » Neural network
