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Found 6 papers, 3 papers with code
Continual Learning Beyond a Single Model
A growing body of research in continual learning focuses on the catastrophic forgetting problem.
Architecture Matters in Continual Learning
However, in this work, we show that the choice of architecture can significantly impact the continual learning performance, and different architectures lead to different trade-offs between the ability to remember previous tasks and learning new ones.
Wide Neural Networks Forget Less Catastrophically
A primary focus area in continual learning research is alleviating the "catastrophic forgetting" problem in neural networks by designing new algorithms that are more robust to the distribution shifts.
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise.
Linear Mode Connectivity in Multitask and Continual Learning
Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks.
Understanding the Role of Training Regimes in Continual Learning
However, there has been limited prior work extensively analyzing the impact that different training regimes -- learning rate, batch size, regularization method-- can have on forgetting.
