9 citations · 11 across the 3 of their papers we have counts for
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cs.LG2026
FlashMoE: Reducing SSD I/O Bottlenecks via ML-Based Cache Replacement for Mixture-of-Experts Inference on Edge Devices
Byeongju Kim, Jungwan Lee, Donghyeon Han +2
Recently, Mixture-of-Experts (MoE) models have gained attention for efficiently scaling large language models. Although these models are extremely large, their sparse activation en…
cs.LG2021★ 9 cited
GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning
Juhyoung Lee, Sangyeob Kim, Sangjin Kim +2
Deep reinforcement learning (DRL) has shown remarkable success in sequential decision-making problems but suffers from a long training time to obtain such good performance. Many pa…