Search Results for author: Shengsheng Huang

Found 4 papers, 2 papers with code

BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster

1 code implementation3 Apr 2022 Jason Dai, Ding Ding, Dongjie Shi, Shengsheng Huang, Jiao Wang, Xin Qiu, Kai Huang, Guoqiong Song, Yang Wang, Qiyuan Gong, Jiaming Song, Shan Yu, Le Zheng, Yina Chen, Junwei Deng, Ge Song

To address this challenge, we have open sourced BigDL 2. 0 at https://github. com/intel-analytics/BigDL/ under Apache 2. 0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2. 0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9. 6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases).

AutoML Distributed Computing +1

Context-Aware Drive-thru Recommendation Service at Fast Food Restaurants

no code implementations13 Oct 2020 Luyang Wang, Kai Huang, Jiao Wang, Shengsheng Huang, Jason Dai, Yue Zhuang

Drive-thru is a popular sales channel in the fast food industry where consumers can make food purchases without leaving their cars.

Recommendation Systems

BigDL: A Distributed Deep Learning Framework for Big Data

2 code implementations16 Apr 2018 Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song

This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.

Fraud Detection Object Detection

SALICON: Saliency in Context

no code implementations CVPR 2015 Ming Jiang, Shengsheng Huang, Juanyong Duan, Qi Zhao

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention.

Saliency Prediction

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