阿里巴巴达摩院 - Must Reading

阿里巴巴达摩院阿里巴巴达摩院发表的相关论文

X-Engine: An Optimized Storage Engine for Large-scale E-commerce Transaction Processing

Gui Huang,Xuntao Cheng, Jianying Wang,Yujie Wang, Dengcheng He, Tieying Zhang,Feifei Li,Sheng Wang,Wei Cao, Qiang Li

Proceedings of the 2019 International Conference on Management of Data, pp.651-665, (2019)

We introduce X-Engine, an OLTP storage engine, optimized for the largest e-commerce platform in the world at Alibaba, serving more than 600 million active customers globally

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Cited by12BibtexViews273DOI

ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

Rana Hanocka,Noa Fish,Zhenhua Wang,Raja Giryes,Shachar Fleishman,Daniel Cohen-Or

ACM Transactions on Graphics (TOG), no. 1 (2019)

Methods

Conclusion

All networks are trained on strictly binary silhouettes, but we overlay a checkerboard texture on the source shape to clearly visualize the smoothness of the estimated mappings and display the texture transfer abilities facilitated by our system

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Cited by11BibtexViews181DOI

iBTune: individualized buffer tuning for large-scale cloud databases

Jian Tan, Tieying Zhang,Feifei Li, Jie Chen, Qixing Zheng,Ping Zhang, Honglin Qiao,Yue Shi,Wei Cao, Rui Zhang

Proceedings of the VLDB Endowment, no. 10 (2019): 1221-1234

Conclusion

We propose iBTune to adjust DBMS buffer pool sizes by using a large deviation analysis for least recently used caching models and leveraging the similar instances based on performance metrics to find tolerable miss ratios

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Cited by8BibtexViews91DOI

AnalyticDB: Real-time OLAP Database System at Alibaba Cloud

Chaoqun Zhan, Maomeng Su, Chuangxian Wei, Xiaoqiang Peng, Liang Lin,Sheng Wang, Zhe Chen,Feifei Li, Yue Pan, Fang Zheng,Chengliang Chai

PVLDB, no. 12 (2019): 2059-2070

Methods

Conclusion

To further improve query latency and concurrency, we enhance the optimizer and execution engine in AnalyticDB to fully utilize the advantages of our storage and indexes

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Cited by2BibtexViews102DOI

Explicit lower bounds on strong simulation of quantum circuits in terms of $T$-gate count

Cupjin Huang, Michael Newman,Mario Szegedy

arXiv: Quantum Physics, (2019)

Conclusion

Assuming the Exponential Time Hypothesis, there is an > 0 such that any strong simulation that can determine if 0|C|0| = 0 of a polynomial-sized quantum circuit C formed from the Clifford+T gate set with N T -gates takes time at least 2 N

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Cited by1BibtexViews118DOI

Optimizing T gates in Clifford+T circuit as $pi/4$ rotations around Paulis

Fang Zhang, Jianxin Chen

arXiv: Quantum Physics, (2019)

Conclusion

We present a new optimization technique to reduce the number of T gates in Clifford+T circuits by treating every gate conjugated by

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Cited by0BibtexViews57

SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks

Chuan Shi,Zhiqiang Zhang, Yugang Ji, Weipeng Wang,Philip S. Yu,Zhiping Shi

World Wide Web, no. 1 (2019): 153-184

Methods

Conclusion

– We propose weighted Heterogeneous Information Network and weighted meta path to consider attribute values on links in information networks

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Cited by0BibtexViews115DOI

Automatic Differentiation for Second Renormalization of Tensor Networks

Chen Bin-Bin, Gao Yuan, Guo Yi-Bin, Liu Yuzhi,Zhao Hui-Hai, Liao Hai-Jun,Wang Lei,Xiang Tao,Li Wei,Xie Z. Y.

Conclusion

Facilitated by the automatic differentiation technique widely used in deep learning, we propose a uniform framework of differentiable Tensor renormalization group that can be applied to improve various Tensor renormalization group methods, in an automatic fashion

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Cited by0BibtexViews120DOI

Alibaba Cloud Quantum Development Platform: Applications to Quantum Algorithm Design

Huang Cupjin,Szegedy Mario, Zhang Fang, Gao Xun, Chen Jianxin,Shi Yaoyun

Conclusion

To compare our Quantum Approximate Optimization Algorithm simulator with existing quantum simulation package equipped with QAOA functionalities, we choose MAX-CUT problems on a random regular graph and compare the time spent for a single energy function query

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Cited by0BibtexViews59DOI

Alibaba Cloud Quantum Development Kit: Large-Scale Classical Simulation of Quantum Circuits

Zhang Fang,Huang Cupjin, Newman Michael, Cai Junjie, Yu Huanjun, Tian Zhengxiong, Yuan Bo, Xu Haihong, Wu Junyin, Gao Xun, Chen Jianxin,Szegedy Mario

Conclusion

Several variables will affect the performance of our simulator:

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Cited by0BibtexViews67DOI

Recent advances in convolutional neural networks

Jiuxiang Gu,Zhenhua Wang, Jason Kuen, Lianyang Ma,Amir Shahroudy,Bing Shuai, Ting Liu, Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen

Pattern Recognition, (2018): 354-377

Conclusion

Beyond surveying the advances of each aspect of Convolutional Neural Network, we have introduced the application of Convolutional Neural Network on many tasks, including image classification, object detection, object tracking, pose estimation, text detection, visual saliency dete...

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Cited by1299BibtexViews263DOI

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

Kai Zhang,Wangmeng Zuo,Lei Zhang

computer vision and pattern recognition, (2018)

Methods

Conclusion

Degradation maps are obtained by a simple dimensionality stretching of the degradation parameters

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Cited by224BibtexViews124DOI

Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates

Jun Liu,Amir Shahroudy,Dong Xu,Alex C. Kot,Gang Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 12 (2018): 3007-3021

Methods

Conclusion

One question that may rise here is if the advantage of “spatio-temporal long short-term memory ” model could be only due to the higher length and redundant sequence of the joints fed to the network, and not because of the proposed semantic relations between the joints

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Cited by178BibtexViews114DOI

Adversarial Attack on Graph Structured Data

Hanjun Dai,Hui Li,Tian Tian,Xin Huang,Lin Wang,Jun Zhu,Le Song

international conference on machine learning, (2018): 1123-1132

Methods

Conclusion

We study the adversarial attack on graph structured data

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Cited by176BibtexViews241DOI

Progressive Attention Guided Recurrent Network for Salient Object Detection

Xiaoning Zhang,Tiantian Wang,Jinqing Qi,Huchuan Lu,Gang Wang

CVPR, pp.714-722, (2018)

Conclusion

We propose a novel progressive attention guided recurrent network, which selectively integrates contextual information from multi-level features to generate powerful attentive features

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Cited by163BibtexViews83DOI

Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation

Henghui Ding,Xudong Jiang,Bing Shuai, Ai Qun Liu,Gang Wang

CVPR, pp.2393-2402, (2018)

Methods

Conclusion

Deep Convolutional Neural Networks designed for image classification tends to extract abstract features of dominated objects, some essentially discriminative information for inconspicuous objects and stuff are weakened or even disregarded

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Cited by148BibtexViews92DOI

Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification

Jianlou Si,Honggang Zhang,Chun-Guang Li, Jason Kuen,Xiangfei Kong,Alex C. Kot,Gang Wang

CVPR, (2018): 5363-5372

Methods

Conclusion

We proposed an end-to-end trainable framework, namely Dual ATtention Matching network, to learn context-aware feature sequences and to perform dually attentive comparison for person ReID

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Cited by148BibtexViews100DOI

Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos

Amir Shahroudy,Tian-Tsong Ng,Yihong Gong,Gang Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 5 (2018): 1045-1058

Methods

Conclusion

This paper presents a new deep learning framework for a hierarchical shared-specific component factorization, to analyze RGB+D features of human action

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Cited by129BibtexViews117DOI

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Jianbo Chen,Le Song,Martin J. Wainwright,Michael I. Jordan

ICML, (2018): 882-891

Methods

Conclusion

We have proposed a framework for instancewise feature selection via mutual information, and a method L2X which seeks a variational approximation of the mutual information, and makes use of a Gumbel-softmax relaxation of discrete subset sampling during training

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Cited by128BibtexViews192DOI

Stochastic Training of Graph Convolutional Networks

Jianfei Chen,Jun Zhu

international conference on machine learning, (2018)

Methods

Conclusion

Our algorithms can achieve comparable convergence speed with the exact algorithm even the neighbor sampling size D(l) = 2, so that the per-epoch cost of training Graph convolution networks is comparable with training multi-layer perceptron

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Cited by128BibtexViews134DOI

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Keywords

IJCAI 2020

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