The field of deep learning is moving towards developing more robust and adaptable models, with a focus on regularization techniques and continual learning. Recent research has highlighted the importance of enforcing domain-informed monotonicity in deep neural networks to improve predictions and mitigate overfitting. Additionally, there is a growing understanding of the challenges associated with loss of plasticity in deep continual learning, including the role of spectral collapse and the importance of activation function design. Mathematical frameworks are being developed to understand the underlying mechanisms of loss of plasticity, revealing a fundamental tension between properties that promote generalization in static settings and those that contribute to loss of plasticity in continual learning scenarios. Noteworthy papers in this area include:

• DIM, which proposes a new regularization method to enforce domain-informed monotonicity in deep neural networks.
• Activation Function Design Sustains Plasticity in Continual Learning, which introduces two drop-in nonlinearities to mitigate plasticity loss.
• Barriers for Learning in an Evolving World, which presents a first-principles investigation of loss of plasticity in gradient-based learning.