Search Results for author: Sicheng Wang

Found 7 papers, 5 papers with code

Understanding Bugs in Multi-Language Deep Learning Frameworks

no code implementations5 Mar 2023 Zengyang Li, Sicheng Wang, Wenshuo Wang, Peng Liang, Ran Mo, Bing Li

Third, we found that 28. 6%, 31. 4%, and 16. 0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs.

Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity

no code implementations29 Dec 2020 Jianghao Shen, Sicheng Wang, Zhangyang Wang

For example, our model with only 1 layer of 15 trees can perform comparably with the model in [3] with 2 layers of 2000 trees each.

CERT: Contrastive Self-supervised Learning for Language Understanding

1 code implementation16 May 2020 Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, Pengtao Xie

We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks.

Natural Language Understanding Self-Supervised Learning +1

MedDialog: Two Large-scale Medical Dialogue Datasets

1 code implementation arXiv 2020 Xuehai He, Shu Chen, Zeqian Ju, Xiangyu Dong, Hongchao Fang, Sicheng Wang, Yue Yang, Jiaqi Zeng, Ruisi Zhang, Ruoyu Zhang, Meng Zhou, Penghui Zhu, Pengtao Xie

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.

Segmentation-Aware Image Denoising without Knowing True Segmentation

2 code implementations22 May 2019 Sicheng Wang, Bihan Wen, Junru Wu, DaCheng Tao, Zhangyang Wang

Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step.

Image Denoising Image Restoration +1

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

1 code implementation14 May 2019 Ernest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms.


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