Summary of Parameter Choice and Neuro-symbolic Approaches For Deep Domain-invariant Learning, by Marius-constantin Dinu
Parameter Choice and Neuro-Symbolic Approaches for Deep Domain-Invariant Learning
by Marius-Constantin Dinu
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes neuro-symbolic (NeSy) artificial intelligence (AI) as a solution to the challenges of broad AI systems, which need to generalize well on diverse tasks, understand context, and adapt rapidly to new scenarios. NeSy AI combines symbolic and sub-symbolic paradigms to enable adaptable, generalizable, and more interpretable systems. The development of broad AI requires advancements in domain adaptation (DA), which enables models trained on source domains to effectively generalize to unseen target domains. Traditional approaches often rely on parameter optimization and fine-tuning, but NeSy AI systems use multiple models and methods to generalize to unseen domains and maintain performance across varying conditions. The paper analyzes common DA and NeSy approaches with a focus on deep domain-invariant learning, extending to real-world challenges such as adapting to continuously changing domains and handling large domain gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop broad AI systems that can perform well on diverse tasks, understand context, and adapt rapidly to new scenarios. The approach uses neuro-symbolic AI, which combines symbolic and sub-symbolic paradigms to enable adaptable, generalizable, and more interpretable systems. The paper explores domain adaptation techniques, including deep domain-invariant learning, to help models generalize to unseen domains and maintain performance across varying conditions. |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Optimization
