A theory of initialisation's impact on specialisation*
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| Název: | A theory of initialisation's impact on specialisation* |
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| Autoři: | Jarvis, Devon, Lee, Sebastian, Carla Juliette Domine, Clementine, Saxe, Andrew M., Sarao Mannelli, Stefano, 1992 |
| Zdroj: | JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT. 2025(11) |
| Témata: | analysis of algorithms, online dynamics, deep learning, machine learning |
| Popis: | Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network's tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of localised representations. Our investigation challenges the validity of this assumption. Using theoretical frameworks for the analysis of neural networks, we show a strong dependence of specialisation on the initial condition. More precisely, we show that weight imbalance and high weight entropy can favour specialised solutions. We then apply these insights in the context of continual learning, first showing the emergence of a monotonic relation between task-similarity and forgetting in non-specialised networks. Finally, we show that specialisation by weight imbalance is beneficial on the commonly employed elastic weight consolidation regularisation technique. |
| Popis souboru: | electronic |
| Přístupová URL adresa: | https://research.chalmers.se/publication/549475 https://research.chalmers.se/publication/549475/file/549475_Fulltext.pdf |
| Databáze: | SwePub |
Nájsť tento článok vo Web of Science | Abstrakt: | Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network's tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of localised representations. Our investigation challenges the validity of this assumption. Using theoretical frameworks for the analysis of neural networks, we show a strong dependence of specialisation on the initial condition. More precisely, we show that weight imbalance and high weight entropy can favour specialised solutions. We then apply these insights in the context of continual learning, first showing the emergence of a monotonic relation between task-similarity and forgetting in non-specialised networks. Finally, we show that specialisation by weight imbalance is beneficial on the commonly employed elastic weight consolidation regularisation technique. |
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| ISSN: | 17425468 |
| DOI: | 10.1088/1742-5468/ae1214 |