no code implementations • 8 Sep 2024 • Junjie Zhao, Chengxi Zhang, Min Qin, Peng Yang
The goal of alpha factor mining is to discover indicative signals of investment opportunities from the historical financial market data of assets, which can be used to predict asset returns and gain excess profits.
no code implementations • 19 Jul 2024 • Stephen M. Pizer, Zhiyuan Liu, Junjie Zhao, Nicholas Tapp-Hughes, James Damon, Miaomiao Zhang, JS Marron, Jared Vicory
We describe a representation targeted for anatomic objects which is designed to enable strong locational correspondence within object populations and thus to provide powerful object statistics.
no code implementations • 18 Apr 2024 • Shunpan Liang, Junjie Zhao, Chen Li, Yu Lei
This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations.
no code implementations • 10 Dec 2023 • Xiaoyang Chen, Junjie Zhao, Siyuan Liu, Sahar Ahmad, Pew-Thian Yap
Moreover, this mapping is possible only if the topology of the surface mesh is homotopic to a sphere.
no code implementations • 23 Apr 2023 • Junjie Zhao
This enabled us to obtain detailed quantitative index data of the degree of influence [10][12][14].
1 code implementation • 9 Nov 2020 • Heming Xia, Lijing Shao, Junjie Zhao, Zhoujian Cao
We point out that CNN models are robust to the variation of the parameter range of the GW waveform.
1 code implementation • ECCV 2020 • Junjie Zhao, Donghuan Lu, Kai Ma, Yu Zhang, Yefeng Zheng
In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.
1 code implementation • 1 Jul 2019 • Junjie Zhao, Lijing Shao, Zhoujian Cao, Bo-Qiang Ma
We investigate the scalar-tensor gravity of Damour and Esposito-Far\`ese (DEF), which predicts non-trivial phenomena in the nonperturbative strong-field regime for neutron stars (NSs).
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena High Energy Physics - Phenomenology
no code implementations • 30 Sep 2017 • Xiangteng He, Yuxin Peng, Junjie Zhao
Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation.
no code implementations • 25 Sep 2017 • Xiangteng He, Yuxin Peng, Junjie Zhao
Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers.
1 code implementation • 6 Apr 2017 • Yuxin Peng, Xiangteng He, Junjie Zhao
Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories.