1 code implementation • 5 May 2024 • Tianze Xu, Jiajun Li, Xuesong Chen, Xinrui Yao, Shuchang Liu
In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the generation of music, images, and other forms of artistic expression across various industries.
1 code implementation • 28 Mar 2024 • Tianhao Zhou, Haipeng Li, Ziyi Wang, Ao Luo, Chen-Lin Zhang, Jiajun Li, Bing Zeng, Shuaicheng Liu
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
1 code implementation • 31 Mar 2023 • YiXuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang, Jiajun Li, Xin Chen, Chao Huang, Wenchao Li, Qi Zhu
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
no code implementations • 11 Mar 2022 • Junhua Ma, Jiajun Li, Xueming Li, Xu Li
To address these problems, we propose a model called PathSAGE, which can learn high-order topological information and improve the model's performance by expanding the receptive field.
no code implementations • 11 Mar 2022 • Junhua Ma, Jiajun Li, Yuxuan Liu, Shangbo Zhou, Xue Li
Recent progress on parse tree encoder for sentence representation learning is notable.
no code implementations • 8 Mar 2022 • Ruijie Yan, Shuang Peng, Haitao Mi, Liang Jiang, Shihui Yang, Yuchi Zhang, Jiajun Li, Liangrui Peng, Yongliang Wang, Zujie Wen
Building robust and general dialogue models for spoken conversations is challenging due to the gap in distributions of spoken and written data.
2 code implementations • 25 Jun 2021 • Chao Huang, Jiameng Fan, Zhilu Wang, YiXuan Wang, Weichao Zhou, Jiajun Li, Xin Chen, Wenchao Li, Qi Zhu
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
1 code implementation • 11 Aug 2018 • Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang
To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.
no code implementations • 10 Oct 2016 • Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.