Summary of Simple and Effective Transfer Learning For Neuro-symbolic Integration, by Alessandro Daniele et al.
Simple and Effective Transfer Learning for Neuro-Symbolic Integration
by Alessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini
First submitted to arxiv on: 21 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 This paper proposes a new approach to improve Neuro-Symbolic Integration (NeSy) models, which combine neural networks with symbolic reasoning. NeSy has shown promising results in generalization tasks, but existing methods face challenges such as slow convergence and difficulty learning complex perception tasks. The proposed method involves pretraining a neural network on the downstream task and then fine-tuning it via transfer learning. This approach demonstrates consistent improvements over state-of-the-art (SOTA) NeSy methods and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to make machines smarter by combining two ways of thinking: like humans do, with both numbers and words. Right now, these “neuro-symbolic” models are good at some things but not others. They can learn from pictures or sounds, but struggle when they need to use that information to solve a problem. The idea is to train the machine to do one task really well first, and then help it learn another task by using what it already knows. This makes the machine better at both tasks and helps it understand things more clearly. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Neural network * Pretraining * Transfer learning
