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Related Questions
- What is catastrophic forgetting in the context of machine learning models, and how does it impact their ability to perform on rare or specialized words?
- Can you explain why machine learning models are prone to catastrophic forgetting, and what are some strategies to mitigate this issue?
- How does catastrophic forgetting affect a model's performance on out-of-distribution words, and what are some approaches to improving its generalizability?
- What is the relationship between catastrophic forgetting and overfitting, and how can a model's architecture be designed to minimize both phenomena?
- Can you provide examples of real-world applications where catastrophic forgetting is a significant concern, and how it can be addressed?
- How does the concept of catastrophic forgetting relate to the idea of 'stability-plasticity dilemma' in neural networks, and what are the implications for model training and deployment?
- What are some techniques for preventing catastrophic forgetting in models that are trained on sequential data, such as language models or time-series forecasting models?
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