4 papers · 1 filter
Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics
Andreas Pattichis, Constantine Dovrolis
LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicitly rather than letting it adap…
Gradual Capacity Growth for Sparse Network Discovery
Qihang Yao, Constantine Dovrolis
Sparse neural network methods typically assume that the target sparsity (or density) is fixed in advance, even though the relationship between network capacity and performance is g…
PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity
Mustafa Burak Gurbuz, Xingyu Zheng, Constantine Dovrolis
As deep learning continues to be driven by ever-larger datasets, understanding which examples are most important for generalization has become a critical question. While progress i…
Patch-Based Contrastive Learning and Memory Consolidation for Online Unsupervised Continual Learning
Cameron Taylor, Vassilis Vassiliades, Constantine Dovrolis
We focus on a relatively unexplored learning paradigm known as {\em Online Unsupervised Continual Learning} (O-UCL), where an agent receives a non-stationary, unlabeled data stream…