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Related Questions
- Can larger model architecture sizes lead to over-specialization in learning domain-specific features?
- To what extent can smaller architecture sizes limit a model's ability to generalize across multiple datasets?
- Can the use of transfer learning help mitigate the trade-offs between architecture size and the ability to learn domain-specific features?
- Does the choice of optimizer have a significant impact on how well a model can adapt to diverse datasets?
- How can the effect of architecture size on performance be measured, and which metrics are most relevant to this trade-off?
- Can ensembling of multiple models with different sizes and architectures improve overall generalizability across domains?
- Is there an architecture size that provides a sweetspot between generalizability and computational resources?
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