Search Results for author: Tianyu Fu

Found 9 papers, 2 papers with code

Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition

no code implementations ECCV 2020 Xiaobo Wang, Tianyu Fu, Shengcai Liao, Shuo Wang, Zhen Lei, Tao Mei

Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.

Face Recognition Knowledge Distillation +1

Representation Learning for Frequent Subgraph Mining

no code implementations22 Feb 2024 Rex Ying, Tianyu Fu, Andrew Wang, Jiaxuan You, Yu Wang, Jure Leskovec

SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.

Representation Learning Subgraph Counting

FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

no code implementations8 Jan 2024 Shulin Zeng, Jun Liu, Guohao Dai, Xinhao Yang, Tianyu Fu, Hongyi Wang, Wenheng Ma, Hanbo Sun, Shiyao Li, Zixiao Huang, Yadong Dai, Jintao Li, Zehao Wang, Ruoyu Zhang, Kairui Wen, Xuefei Ning, Yu Wang

However, existing GPU and transformer-based accelerators cannot efficiently process compressed LLMs, due to the following unresolved challenges: low computational efficiency, underutilized memory bandwidth, and large compilation overheads.

Computational Efficiency Language Modelling +2

DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting

1 code implementation16 Aug 2023 Tianyu Fu, Chiyue Wei, Yu Wang, Rex Ying

We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training.

Graph Regression Position +1

Mis-classified Vector Guided Softmax Loss for Face Recognition

no code implementations26 Nov 2019 Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, Tao Mei

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.

Face Recognition

Improved Selective Refinement Network for Face Detection

no code implementations20 Jan 2019 Shifeng Zhang, Rui Zhu, Xiaobo Wang, Hailin Shi, Tianyu Fu, Shuo Wang, Tao Mei, Stan Z. Li

With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.

Data Augmentation Face Detection +1

Support Vector Guided Softmax Loss for Face Recognition

3 code implementations29 Dec 2018 Xiaobo Wang, Shuo Wang, Shifeng Zhang, Tianyu Fu, Hailin Shi, Tao Mei

Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.

Face Recognition

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