no code implementations • Findings (ACL) 2022 • Zuchao Li, Yiran Wang, Masao Utiyama, Eiichiro Sumita, Hai Zhao, Taro Watanabe
Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT.
no code implementations • 17 Mar 2024 • Yiran Wang, Yimin Zhong
The limited angle Radon transform is notoriously difficult to invert due to the ill-posedness.
no code implementations • 12 Mar 2024 • Yiran Wang, Li Xiao
It has been shown that traditional deep learning methods for electronic microscopy segmentation usually suffer from low transferability when samples and annotations are limited, while large-scale vision foundation models are more robust when transferring between different domains but facing sub-optimal improvement under fine-tuning.
no code implementations • 13 Sep 2023 • Yiran Wang, Suchuan Dong
With the second method the high-dimensional PDE problem is reformulated through a constrained expression based on an Approximate variant of the Theory of Functional Connections (A-TFC), which avoids the exponential growth in the number of terms of TFC as the dimension increases.
no code implementations • 8 Aug 2023 • Yiran Wang, Haiwang Zhong, Guangchun Ruan
Aggregating distributed energy resources (DERs) is of great significance to improve the overall operational efficiency of smart grid.
no code implementations • 4 Aug 2023 • Jiaqi Li, Yiran Wang, Zihao Huang, Jinghong Zheng, Ke Xian, Zhiguo Cao, Jianming Zhang
We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.
3 code implementations • ICCV 2023 • Yiran Wang, Min Shi, Jiaqi Li, Zihao Huang, Zhiguo Cao, Jianming Zhang, Ke Xian, Guosheng Lin
Video depth estimation aims to infer temporally consistent depth.
Ranked #16 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
no code implementations • 15 Jun 2023 • Yunfan Li, Yiran Wang, Yu Cheng, Lin Yang
We show that, our algorithm obtains an $\varepsilon$-optimal policy with only $\widetilde{O}(\frac{\text{poly}(d)}{\varepsilon^3})$ samples, where $\varepsilon$ is the suboptimality gap and $d$ is a complexity measure of the function class approximating the policy.
no code implementations • 7 Nov 2022 • Andrey Ignatov, Grigory Malivenko, Radu Timofte, Lukasz Treszczotko, Xin Chang, Piotr Ksiazek, Michal Lopuszynski, Maciej Pioro, Rafal Rudnicki, Maciej Smyl, Yujie Ma, Zhenyu Li, Zehui Chen, Jialei Xu, Xianming Liu, Junjun Jiang, XueChao Shi, Difan Xu, Yanan Li, Xiaotao Wang, Lei Lei, Ziyu Zhang, Yicheng Wang, Zilong Huang, Guozhong Luo, Gang Yu, Bin Fu, Jiaqi Li, Yiran Wang, Zihao Huang, Zhiguo Cao, Marcos V. Conde, Denis Sapozhnikov, Byeong Hyun Lee, Dongwon Park, Seongmin Hong, Joonhee Lee, Seunggyu Lee, Se Young Chun
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
no code implementations • 9 Oct 2022 • Suchuan Dong, Yiran Wang
The presented method has been compared with the physics-informed neural network method.
1 code implementation • 29 Sep 2022 • Xingyi Li, Chaoyi Hong, Yiran Wang, Zhiguo Cao, Ke Xian, Guosheng Lin
We study the problem of novel view synthesis of objects from a single image.
1 code implementation • 31 Jul 2022 • Yiran Wang, Zhiyu Pan, Xingyi Li, Zhiguo Cao, Ke Xian, Jianming Zhang
Temporal consistency is the key challenge of video depth estimation.
1 code implementation • ACL 2021 • Yiran Wang, Hiroyuki Shindo, Yuji Matsumoto, Taro Watanabe
This paper presents a novel method for nested named entity recognition.
Ranked #12 on Nested Named Entity Recognition on ACE 2005
no code implementations • 19 Jul 2021 • Yiran Wang, Zhen Li
In this work, we use an explainable convolutional neural network (NLS-Net) to solve an inverse problem of the nonlinear Schr\"odinger equation, which is widely used in fiber-optic communications.
no code implementations • 17 May 2021 • Andrey Ignatov, Grigory Malivenko, David Plowman, Samarth Shukla, Radu Timofte, Ziyu Zhang, Yicheng Wang, Zilong Huang, Guozhong Luo, Gang Yu, Bin Fu, Yiran Wang, Xingyi Li, Min Shi, Ke Xian, Zhiguo Cao, Jin-Hua Du, Pei-Lin Wu, Chao Ge, Jiaoyang Yao, Fangwen Tu, Bo Li, Jung Eun Yoo, Kwanggyoon Seo, Jialei Xu, Zhenyu Li, Xianming Liu, Junjun Jiang, Wei-Chi Chen, Shayan Joya, Huanhuan Fan, Zhaobing Kang, Ang Li, Tianpeng Feng, Yang Liu, Chuannan Sheng, Jian Yin, Fausto T. Benavide
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
no code implementations • WS 2019 • Xinze Guo, Chang Liu, Xiaolong Li, Yiran Wang, Guoliang Li, Feng Wang, Zhitao Xu, Liuyi Yang, Li Ma, Changliang Li
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
no code implementations • 9 Nov 2018 • Gunther Uhlmann, Yiran Wang
We study inverse problems consisting on determining medium properties using the responses to probing waves from the machine learning point of view.