Problems, papers, reviews, and tags.
Continual learning systems must remain plastic enough to learn new tasks while stable enough to avoid catastrophic forgetting, but this tradeoff is increasingly constrained by memo…
We propose the problem of designing a dynamic, continuously-adaptive activation function that embeds continual-learning inductive biases directly into neuron nonlinearity. The targ…
Continual learning methods frequently reduce forgetting by adding replay, regularization, or routing constraints, yet activation functions are usually treated as fixed nonlineariti…
Continual learning systems are increasingly limited by memory behavior rather than arithmetic throughput: the same memory substrate must support stable recall and adaptive updates…
Continual learning systems are increasingly deployed in settings where data distributions evolve while labels, environments, and downstream requirements remain nonstationary. In th…
