Summary of Learning New Concepts, Remembering the Old: a Novel Continual Learning, by Songning Lai et al.
Learning New Concepts, Remembering the Old: A Novel Continual Learning
by Songning Lai, Mingqian Liao, Zhangyi Hu, Jiayu Yang, Wenshuo Chen, Yutao Yue
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed Conceptual Continual Incremental Learning (CONCIL) framework enhances the interpretability of Concept Bottleneck Models (CBMs) by enabling them to learn new concepts and classes over time while retaining previously learned knowledge. This is achieved through a novel reformulation of concept and decision layer updates as linear regression problems, eliminating the need for gradient-based updates. The framework requires only recursive matrix operations, making it computationally efficient and suitable for real-time and large-scale data applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Concept Bottleneck Models (CBMs) are special kinds of artificial intelligence that help people understand how they make decisions. Right now, these models can’t learn new things over time without forgetting what they already knew. The researchers came up with a new way to teach CBMs called CONCIL, which helps them remember and learn at the same time. This is important because it means we can use these AI models in real-life situations where data is constantly changing. |
Keywords
» Artificial intelligence » Linear regression
