Search Results for author: Liang Shen

Found 14 papers, 3 papers with code

Reinforced Decoder: Towards Training Recurrent Neural Networks for Time Series Forecasting

no code implementations14 Jun 2024 Qi Sima, Xinze Zhang, Yukun Bao, Siyue Yang, Liang Shen

These models typically involve a decoder trained using either its previous forecasts or the actual observed values as the decoder inputs.

SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System

1 code implementation20 May 2022 Liang Shen, Zhihua Wu, Weibao Gong, Hongxiang Hao, Yangfan Bai, HuaChao Wu, Xinxuan Wu, Jiang Bian, Haoyi Xiong, dianhai yu, Yanjun Ma

With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.

Distributed Computing

An Empirical Study of Low Precision Quantization for TinyML

no code implementations10 Mar 2022 Shaojie Zhuo, Hongyu Chen, Ramchalam Kinattinkara Ramakrishnan, Tommy Chen, Chen Feng, Yicheng Lin, Parker Zhang, Liang Shen

In this study, we focus on post-training quantization (PTQ) algorithms that quantize a model to low-bit (less than 8-bit) precision with only a small set of calibration data and benchmark them on different tinyML use cases.

BIG-bench Machine Learning Model Compression +1

End-to-end Adaptive Distributed Training on PaddlePaddle

1 code implementation6 Dec 2021 Yulong Ao, Zhihua Wu, dianhai yu, Weibao Gong, Zhiqing Kui, Minxu Zhang, Zilingfeng Ye, Liang Shen, Yanjun Ma, Tian Wu, Haifeng Wang, Wei Zeng, Chao Yang

The experiments demonstrate that our framework can satisfy various requirements from the diversity of applications and the heterogeneity of resources with highly competitive performance.

Language Modelling Recommendation Systems +1

Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm

no code implementations31 May 2020 Xiaotao Huang, Liang Shen, Chongyi Fan, Jiahua zhu, Sixian Chen

Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions.

Novel Co-variant Feature Point Matching Based on Gaussian Mixture Model

no code implementations26 Oct 2019 Liang Shen, Jiahua zhu, Chongyi Fan, Xiaotao Huang, Tian Jin

In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching.

Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution

no code implementations12 Mar 2019 Chen Feng, Tao Sheng, Zhiyu Liang, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Matthew Ardi, Alexander C. Berg, Yiran Chen, Bo Chen, Kent Gauen, Yung-Hsiang Lu

The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.


Radiative Transport Based Flame Volume Reconstruction from Videos

no code implementations17 Sep 2018 Liang Shen, Dengming Zhu, Saad Nadeem, Zhaoqi Wang, Arie Kaufman

The approach includes an economical data capture technique using inexpensive CCD cameras.

A Quantization-Friendly Separable Convolution for MobileNets

1 code implementation22 Mar 2018 Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Mickey Aleksic

As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.

Edge-computing Image Classification +2

Deep joint rain and haze removal from single images

no code implementations21 Jan 2018 Liang Shen, Zihan Yue, Quan Chen, Fan Feng, Jie Ma

On the other hand, the accumulation of rain streaks from long distance makes the rain image look like haze veil.

Rain Removal

MSR-net:Low-light Image Enhancement Using Deep Convolutional Network

no code implementations7 Nov 2017 Liang Shen, Zihan Yue, Fan Feng, Quan Chen, Shihao Liu, Jie Ma

In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed.

Low-Light Image Enhancement

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