1 code implementation • 15 Mar 2024 • Jiarui Li, Ye Yuan, Zehua Zhang
We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases.
1 code implementation • 24 Feb 2024 • Zehua Zhang, Zijie Li, Amir Barati Farimani
We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems.
1 code implementation • 25 Oct 2022 • Zehua Zhang, Shilin Sun, Guixiang Ma, Caiming Zhong
Link prediction tasks focus on predicting possible future connections.
no code implementations • 15 Mar 2022 • Zehua Zhang, Lu Zhang, Xuyi Zhuang, Yukun Qian, Heng Li, Mingjiang Wang
In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement.
no code implementations • 9 Jul 2021 • Lu Zhang, Mingjiang Wang, Andong Li, Zehua Zhang, Xuyi Zhuang
In this study, we make full use of the contribution of multi-target joint learning to the model generalization capability, and propose a lightweight and low-computing dilated convolutional network (DCN) model for a more robust speech denoising task.
no code implementations • 9 Jun 2021 • Lu Zhang, Mingjiang Wang, Zehua Zhang, Xuyi Zhuang
In this paper, we propose a multi-branch dilated convolutional network (DCN) to simultaneously enhance the magnitude and phase of noisy speech.
no code implementations • 23 Nov 2020 • Zehua Zhang, David Crandall
We present a novel technique for self-supervised video representation learning by: (a) decoupling the learning objective into two contrastive subtasks respectively emphasizing spatial and temporal features, and (b) performing it hierarchically to encourage multi-scale understanding.
no code implementations • NeurIPS 2020 • Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
no code implementations • 18 Jun 2020 • Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
no code implementations • 12 Mar 2020 • Zehua Zhang, Ashish Tawari, Sujitha Martin, David Crandall
A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions.
1 code implementation • NeurIPS 2019 • Zehua Zhang, Chen Yu, David Crandall
Due to the foveated nature of the human vision system, people can focus their visual attention on a small region of their visual field at a time, which usually contains only a single object.