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cs.LG2026
MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
Nurbek Tastan, Stefanos Laskaridis, Karthik Nandakumar +1
Mixture-of-Experts (MoE) models scale large language models efficiently by sparsely activating experts, but once an expert is selected, it is executed fully. Hence, the trade-off b…
cs.LG2026
FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
Riccardo Zaccone, Stefanos Laskaridis, Marco Ciccone +1
The growing scale of deep neural networks, encompassing large language models (LLMs) and vision transformers (ViTs), has made training from scratch prohibitively expensive and depl…