1 code implementation • 5 May 2024 • Zhixiang Su, Yinan Zhang, Jiazheng Jing, Jie Xiao, Zhiqi Shen
Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measures.
no code implementations • 20 Jan 2024 • Yinan Zhang, Eric Tzeng, Yilun Du, Dmitry Kislyuk
Text-to-image diffusion models are a class of deep generative models that have demonstrated an impressive capacity for high-quality image generation.
1 code implementation • 17 Aug 2023 • Jiazheng Jing, Yinan Zhang, Xin Zhou, Zhiqi Shen
To our knowledge, this is the first work to explicitly model item popularity in recommendation systems.
no code implementations • 29 Jun 2022 • Yinan Zhang, Boyang Li, Yong liu, You Yuan, Chunyan Miao
Multi-shot CRS is designed to make recommendations multiple times until the user either accepts the recommendation or leaves at the end of their patience.
no code implementations • 15 Jun 2022 • Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosič, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec
To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.
1 code implementation • 8 Apr 2022 • Yinan Zhang, Pei Wang, Congcong Liu, Xiwei Zhao, Hao Qi, Jie He, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao
In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN).
no code implementations • 9 Jun 2021 • Yinan Zhang, Boyang Li, Yong liu, Hao Wang, Chunyan Miao
In this work, we propose a new initialization scheme for user and item embeddings called Laplacian Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID).
1 code implementation • ICCV 2021 • Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenenbaum, Jiajun Wu
We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images.
no code implementations • ICLR 2021 • Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler
Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties.
no code implementations • 24 Apr 2020 • Yong liu, Susen Yang, Yinan Zhang, Chunyan Miao, Zaiqing Nie, Juyong Zhang
Therefore, they may not be effective in capturing the global dependency between words, and tend to be easily biased by noise review information.
no code implementations • 20 Feb 2020 • Yinan Zhang, Parikshit Sondhi, Anjan Goswami, ChengXiang Zhai
Faceted browsing is a commonly supported feature of user interfaces for access to information.
no code implementations • 7 Nov 2019 • Yinan Zhang, Raphael Tang, Jimmy Lin
In this paper, we hypothesize that introducing an explicit, constrained pairwise word interaction mechanism to pretrained language models improves their effectiveness on semantic similarity tasks.
no code implementations • 19 Mar 2019 • Yong Liu, Yinan Zhang, Qiong Wu, Chunyan Miao, Lizhen Cui, Binqiang Zhao, Yin Zhao, Lu Guan
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
no code implementations • 19 Jan 2019 • Yinan Zhang, Devin Balkcom, Haoxiang Li
A weighted average of the supervisor and learned policies is used during trials, with a heavier weight initially on the supervisor, in order to allow safe and useful physical trials while the learned policy is still ineffective.
no code implementations • 21 Jun 2018 • Ching Tarn, Yinan Zhang, Ye Feng
First a subset of vertices of the graph are selected as representatives to build a concise graph.