activity
20182026
most citedAn Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

SPADE: An Input-Adaptive Sparse Attention Engine for Fast Video Diffusion Models Inference

Shanghao Liu, Renze Chen, Size Zheng +4

Video diffusion transformers (vDiTs) generate high quality but pay quadratic self-attention cost, making inference prohibitive at video-token scales. The challenge is input-adaptiv…

cs.PL2026

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

Size Zheng, Xuegui Zheng, Hanshi Sun +16

The scaling of large language models (LLMs) is currently bottlenecked by the rigidity of distributed programming. While high-performance libraries like CuBLAS and NCCL provide opti…

cs.SE20215 cited

An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications

Zhenpeng Chen, Huihan Yao, Yiling Lou +4

Deep Learning (DL) is finding its way into a growing number of mobile software applications. These software applications, named as DL based mobile applications (abbreviated as mobi…

cs.SE2020

A Comprehensive Study on Challenges in Deploying Deep Learning Based Software

Zhenpeng Chen, Yanbin Cao, Yuanqiang Liu +3

Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications. These software applications, named as DL based software (in short as DL soft…

cs.LG2018

A First Look at Deep Learning Apps on Smartphones

Mengwei Xu, Jiawei Liu, Yuanqiang Liu +3

We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Andr…