4 papers
On the Stability of Growth in Structural Plasticity
Lute Lillo, Nick Cheney
Standard deep-learning pipelines usually choose the network architecture before training and keep it fixed throughout optimization. In contrast, a model can also be adapted by edit…
Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
Lute Lillo, Nick Cheney
Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the m…
Activation Function Design Sustains Plasticity in Continual Learning
Lute Lillo, Nick Cheney
In independent, identically distributed (i.i.d.) training regimes, activation functions have been benchmarked extensively, and their differences often shrink once model size and op…
Solving Epistemic Logic Programs using Generate-and-Test with Propagation
Jorge Fandinno, Lute Lillo
This paper introduces a general framework for generate-and-test-based solvers for epistemic logic programs that can be instantiated with different generator and tester programs, an…