แนวคิดหลัก
Increasing the width of neural networks can lead to diminishing returns in improving continual learning, as observed empirically and theoretically. The relationship between width and continual learning error is complex and influenced by various factors.
บทคัดย่อ
The content explores the impact of increasing network width on continual learning in neural networks. It discusses theoretical frameworks, empirical observations, experiments on various datasets, and the connection between width, depth, sparsity, and forgetting. The findings suggest that while wider models may initially reduce forgetting, there are diminishing returns at larger widths.
Several experiments were conducted across different datasets to validate the theoretical analysis. Results show that increasing width helps improve forgetting early on but reaches a plateau at larger widths. The study also examines the effects of depth, sparsity, and number of tasks on continual learning error. Overall, the research provides valuable insights into optimizing neural network architectures for continual learning.
Key points include:
Increasing network width can lead to diminishing returns in improving continual learning.
Theoretical frameworks connect width to forgetting in Feed-Forward Networks.
Empirical experiments demonstrate diminishing returns at larger hidden dimensions.
Sparsity can significantly decrease average forgetting in neural networks.
สถิติ
Increasing model depth or number of tasks will increase continual learning error.
Increasing row-wise sparsity decreases continual learning error.
Forgetting decreases slowly as distance from initialization decreases with increasing width.
คำพูด
"Empirically verify this relationship on Feed-Forward Networks trained with either Stochastic Gradient Descent (SGD) or Adam."
"Our results contribute to examining the relationship between neural network architectures and continual learning performance."