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

Yanhao Zhang, Pan Pan, Yun Zheng,Kang Zhao, Yingya Zhang,Xiaofeng Ren,Rong Jin

KDD, pp.993-1001, (2021)

The deep CNN model with branches is designed for joint detection and feature learning by mining user click behavior without further annotations

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Cited by49BibtexViews25DOI

Heterogeneous Graph Neural Networks for Malicious Account Detection

Ziqi Liu,Chaochao Chen, Xinxing Yang,Jun Zhou,Xiaolong Li,Le Song

CIKM, pp.2077-2085, (2020)

Methods

Conclusion

We summarize two fundamental weaknesses of attackers, namely “Device aggregation” and “Activity aggregation”, and naturally present a neural network approach based on heterogeneous account-device graphs

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Cited by35BibtexViews235

Motion-Guided Cascaded Refinement Network for Video Object Segmentation

Ping Hu,Gang Wang,Xiangfei Kong, Jason Kuen,Yap-Peng Tan

CVPR, pp.1400-1409, (2020)

Methods

Conclusion

We present a Cascaded Refinement Network that generate accurate segmentations under the guidance of coarse results from the optical flow-based active contour

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Cited by23BibtexViews106

Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization

Chaochao Chen,Ziqi Liu,Peilin Zhao,Jun Zhou,Xiaolong Li

THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTI..., pp.257-264, (2020)

Methods

Conclusion

We proposed a Decentralized Matrix Factorization framework for Points of interest recommendation

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Cited by11BibtexViews73

Unpack Local Model Interpretation for GBDT

Wenjing Fang,Jun Zhou,Xiaolong Li,Kenny Q. Zhu

DASFAA, pp.764-775, (2020)

Conclusion

We describe a method to unpack the interpretation for the advanced model gradient boosting decision tree

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Cited by2BibtexViews11

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.

Physical Review B, no. 22 (2020)

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 by0BibtexViews327DOI

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 by17BibtexViews376DOI

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)

Methods

Conclusion

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 by12BibtexViews548DOI

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 by8BibtexViews268DOI

Multinomial Logit Bandit with Linear Utility Functions

Mingdong Ou,Nan Li,Shenghuo Zhu,Rong Jin

IJCAI, (2019)

Methods

Conclusion

We study the sequential subset selection problem with linear utility multinomial logit choice model, and propose a UCBstyle algorithm, Linear Utility MNL-Bandit

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Cited by5BibtexViews28

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 by2BibtexViews260DOI

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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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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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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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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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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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 by1582BibtexViews646DOI

Learning Combinatorial Optimization Algorithms over Graphs

Hanjun Dai, Elias B. Khalil,Yuyu Zhang,Bistra Dilkina,Le Song

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), (2018)

Conclusion

We presented an end-to-end machine learning framework for automatically designing greedy heuristics for hard combinatorial optimization problems on graphs

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Cited by365BibtexViews83

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 by338BibtexViews264DOI

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 by268BibtexViews183

Keywords

ICLR 2021

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