1 code implementation • 4 Feb 2022 • Boyuan Chen, Mingzhi Wen, Yong Shi, Dayi Lin, Gopi Krishnan Rajbahadur, Zhen Ming, Jiang
However, DL models are challenging to be reproduced due to issues like randomness in the software (e. g., DL algorithms) and non-determinism in the hardware (e. g., GPU).
no code implementations • 30 Sep 2021 • Minglong Lei, Yong Shi, Lingfeng Niu
To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders.
no code implementations • 27 Sep 2021 • Jie Yang, Ruijie Xu, Zhiquan Qi, Yong Shi
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision.
no code implementations • 26 May 2021 • Yong Shi, Wei Dai, Wen Long, Bo Li
However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge.
no code implementations • 3 Mar 2021 • Huanxue Feng, Junzhi Wang, Shanghuo Li, Yong Shi, Fengyao Zhu, Minzhi Kong, Ripeng Gao, Fei Li
We performed observations of the HC$_3$N (24-23, 17-16, 11-10, 8-7) lines towards a sample consisting of 19 Galactic massive star-forming regions with the Arizona Radio Observatory 12-m and Caltech Submillimeter Observatory 10. 4-m telescopes.
Astrophysics of Galaxies Solar and Stellar Astrophysics
no code implementations • 3 Feb 2021 • Yong Shi, Bo Li, Guangle Du
Artificial stock market simulation based on agent is an important means to study financial market.
no code implementations • 7 Jan 2021 • Yong Shi, Wei Dai, Wen Long, Bo Li
In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.
2 code implementations • 13 Dec 2020 • Jie Yang, Yong Shi, Zhiquan Qi
Concretely, we develop a multi-scale regional feature generator that can generate multiple spatial context-aware representations from pre-trained deep convolutional networks for every subregion of an image.
Ranked #94 on
Anomaly Detection
on MVTec AD
1 code implementation • 11 Feb 2020 • Jie Yang, Zhiquan Qi, Yong Shi
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works.
no code implementations • ICLR 2020 • Yong Shi, Biao Li, Bo wang, Zhiquan Qi, Jiabin Liu, Fan Meng
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.
1 code implementation • 24 Sep 2019 • Biao Li, Jiabin Liu, Bo Wang, Zhiquan Qi, Yong Shi
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance.
1 code implementation • NeurIPS 2019 • Jiabin Liu, Bo wang, Zhiquan Qi, Yingjie Tian, Yong Shi
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available.
no code implementations • 19 Dec 2018 • Yong Shi, Huadong Wang, Xin Shen, Lingfeng Niu
Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels.
no code implementations • 6 Sep 2018 • Zilong Lin, Yong Shi, Zhi Xue
Given that the internal structure and parameters of the detection system are unknown to attackers, the adversarial attack examples perform the black-box attacks against the detection system.
no code implementations • 9 Jun 2018 • Feng Liu, Yong Shi
The unclear development direction of human society is a deep reason for that it is difficult to form a uniform ethical standard for human society and artificial intelligence.
no code implementations • 9 May 2018 • Yong Shi, Minglong Lei, Peng Zhang, Lingfeng Niu
In order to solve the limitations, we propose in this paper a network diffusion based embedding method.
no code implementations • 8 Jan 2018 • Yunlong Mi, Yong Shi, Jinhai Li
However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described.
no code implementations • 14 Dec 2017 • Feng Liu, Yong Shi, Ying Liu
The rapid development of artificial intelligence has brought the artificial intelligence threat theory as well as the problem about how to evaluate the intelligence level of intelligent products.
no code implementations • 29 Sep 2017 • Feng Liu, Yong Shi, Ying Liu
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy.
no code implementations • IEEE Transactions on Intelligent Transportation Systems 2016 • Yong Shi, Limeng Cui, Zhiquan Qi, Fan Meng, and Zhensong Chen
Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high- performance crack detector, which can identify arbitrarily com- plex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively.
no code implementations • 3 Dec 2015 • Feng Liu, Yong Shi
Currently, potential threats of artificial intelligence (AI) to human have triggered a large controversy in society, behind which, the nature of the issue is whether the artificial intelligence (AI) system can be evaluated quantitatively.
no code implementations • 11 Apr 2015 • Feng Liu, Yong Shi
On the basis of traditional IQ, this article presents the Universal IQ test method suitable for both the machine intelligence and the human intelligence.