4 papers · 1 filter
SPARCL: Spectral Partitioned Analytic Continual Learning
James Hartley, Zeropy Surio, Daniel Whitmore +2
Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative optimization with closed-fo…
Uncertainty Is Not Enough: Value-of-Information Routing for Mixtures of LoRA Experts
Tom Saliencro, Rohan Desai, Priya Nair +2
Mixtures of low-rank adaptation experts increase parameter-efficient capacity by routing each input through a subset of adapters. Recent dynamic routers activate more experts when…
Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRA
Tom Saliencro, Rohan Desai, Priya Nair +2
Mixture-of-Experts (MoE) variants of Low-Rank Adaptation (LoRA) route every token to a fixed number of experts . Tokens differ in how uncertain the model is about them, so a sin…
FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts
Tom Saliencro, Maya Lindqvist, Rohan Desai +2
Parameter-efficient fine-tuning (PEFT) reparameterizes weight updates in a fixed basis: low-rank adapters operate in the spatial domain, while a recent line of spectral methods ope…