Open and completed research requests

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 memory behavior rather than compute alone. This project adopts a unified data-access perspective in which deterministic retrieval and stochastic sampling are treated within one framework, so memory support for both adaptation and retention can be analyzed jointly. We will design and evaluate memory mechanisms that explicitly manage stochastic demand, entropy pressure, and replay/update efficiency across task sequences. Expected impact is a clearer systems-level blueprint for continual learning stacks that improve retention and adaptation while remaining robust and efficient on realistic hardware.

We propose the problem of designing a dynamic, continuously-adaptive activation function that embeds continual-learning inductive biases directly into neuron nonlinearity. The target activation should trade off plasticity versus stability so networks can incrementally integrate concepts from multiple domains without catastrophic forgetting, while remaining scalable (avoiding saturation/dead neurons) and self-stabilizing (preserving bounded mean/variance across layers). Crucially, this mechanism must not rely on predefined task counts, explicit task identities, or brittle masking hacks; instead it should fold related concepts into shared representations and enable layer-wise progressive knowledge integration and reuse so that higher-level abstractions and deductive reasoning can emerge.