3 papers
cs.CL2024
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu +3
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we…
cs.LG2023
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor +6
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail whe…
cs.NI2014
Multicast Group Management for Multi-View 3D Videos in Wireless Networks
Chi-Heng Lin, De-Nian Yang, Chih-Chung Lin +1
With the emergence of 3D mobile devices available in the markets, mobile 3D video services become increasingly important for video service providers, such as Youtube and Netflix, w…