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Yongpan Liu

3 accepted papers

2025

MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE

NeurIPS 2025spotlight

Large Language Models (LLMs) have achieved remarkable success across many applications, with Mixture of Experts (MoE) models demonstrating great potential. Compared to traditional dense models, MoEs achieve better performance with less computation. Speculative decoding (SD) is a widely used techniqu…

Cited by 0SourceScholar
2023

SEFormer: Structure Embedding Transformer for 3D Object Detection

AAAI 2023technical

Effectively preserving and encoding structure features from objects in irregular and sparse LiDAR points is a crucial challenge to 3D object detection on the point cloud. Recently, Transformer has demonstrated promising performance on many 2D and even 3D vision tasks. Compared with the fixed and ri…

2020

High-quality Single-model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation

ECCV 2020poster

Deep learning (DL) methods have revolutionized the paradigm of computer vision tasks and DL-based video compression is becoming a hot topic. This paper proposes a deep video compression method to simultaneously encode multiple frames with Frame-Conv3D and differential modulation. We first adopt Fram…

Cited by 15SourcePDFScholar