Summary of Distal Interference: Exploring the Limits Of Model-based Continual Learning, by Heinrich Van Deventer et al.
Distal Interference: Exploring the Limits of Model-Based Continual Learning
by Heinrich van Deventer, Anna Sergeevna Bosman
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 Continual learning, a machine learning technique where a model learns different tasks sequentially, has been hindered by catastrophic interference or forgetting. This phenomenon occurs when a model rapidly unlearns earlier learned tasks while learning new ones. Despite their success, artificial neural networks (ANNs) are prone to this issue. This study investigates how gradient descent and overlapping representations between distant input points lead to distal interference, which is characterized by non-local changes on different subsets of the domain. The authors propose a novel architecture, ABEL-Spline, which can approximate any continuous function, is uniformly trainable, has polynomial computational complexity, and provides guarantees against distal interference. Experiments demonstrate the theoretical properties of ABEL-Splines, while evaluations on benchmark regression problems show that these models are insufficient for model-only continual learning. The authors suggest that augmentation of training data or algorithm may be necessary for polynomial complexity models to achieve effective continual learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about how machines learn new things without forgetting what they already know. Right now, machines have trouble remembering old skills when they learn new ones. The researchers found out that this happens because of the way machines are trained. They created a new kind of machine learning model called ABEL-Spline that can remember old skills and learn new ones without forgetting. They tested this model on some problems and found out it works pretty well, but there’s still more work to do before we can use these machines for real-world applications. |
Keywords
* Artificial intelligence * Continual learning * Gradient descent * Machine learning * Regression
