Search Results for author: Guoping Long

Found 8 papers, 1 papers with code

GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring

1 code implementation12 Apr 2016 Wenying Ma, Liangliang Cao, Lei Yu, Guoping Long, Yucheng Li

We also applied GPU-FV for realtime video monitoring tasks and found that GPU-FV outperforms a number of previous works.

Retrieval

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

no code implementations13 Nov 2018 Guoping Long, Jun Yang, Kai Zhu, Wei. Lin

In recent years, there is a surge on machine learning applications in industry.

Distributed, Parallel, and Cluster Computing Mathematical Software

Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling

no code implementations10 Oct 2019 Changying Du, Fuzhen Zhuang, Jia He, Qing He, Guoping Long

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.

Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning

no code implementations11 Oct 2019 Changying Du, Jia He, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.

Data Augmentation MULTI-VIEW LEARNING

Characterizing Deep Learning Training Workloads on Alibaba-PAI

no code implementations14 Oct 2019 Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei. Lin, Yangqing Jia

One critical issue for efficiently operating practical AI clouds, is to characterize the computing and data transfer demands of these workloads, and more importantly, the training performance given the underlying software framework and hardware configurations.

FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads

no code implementations23 Sep 2020 Zhen Zheng, Pengzhan Zhao, Guoping Long, Feiwen Zhu, Kai Zhu, Wenyi Zhao, Lansong Diao, Jun Yang, Wei. Lin

We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.

Code Generation

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