3 papers
cs.LG2026
Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees
Mohammed Nowaz Rabbani Chowdhury, Kaoutar El Maghraoui, Hsinyu Tsai +4
Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input. While this reduces computation, the…
cs.AR2025
Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System
Yunhua Fang, Rui Xie, Asad Ul Haq +6
Large Language Model (LLM) inference is increasingly constrained by memory bandwidth, with frequent access to the key-value (KV) cache dominating data movement. While attention spa…
cs.CV2025
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media
Ismail Erbas, Ferhat Demirkiran, Karthik Swaminathan +6
Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters si…