1 code implementation • 26 Apr 2024 • Jing Hu, Honghu Zhang, Peng Zheng, Jialin Mu, Xiaomeng Huang, Xi Wu
This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales.
no code implementations • 16 Apr 2024 • Xiaomin Fang, Jie Gao, Jing Hu, Lihang Liu, Yang Xue, Xiaonan Zhang, Kunrui Zhu
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field.
no code implementations • 17 Jan 2024 • Chengxu Wu, Qinrui Fan, Shu Hu, Xi Wu, Xin Wang, Jing Hu
An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms.
Ranked #53 on Image Super-Resolution on Set14 - 4x upscaling
no code implementations • 10 Nov 2023 • Jing Hu, Qinrui Fan, Shu Hu, Siwei Lyu, Xi Wu, Xin Wang
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies.
no code implementations • 30 Sep 2023 • Chengming Feng, Jing Hu, Xin Wang, Shu Hu, Bin Zhu, Xi Wu, Hongtu Zhu, Siwei Lyu
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters.
1 code implementation • 24 Sep 2023 • Xin Wang, Ziwei Luo, Jing Hu, Chengming Feng, Shu Hu, Bin Zhu, Xi Wu, Xin Li, Siwei Lyu
The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
1 code implementation • 13 Jul 2023 • Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, Furu Wei
We propose the In-context Autoencoder (ICAE), leveraging the power of a large language models (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes.
no code implementations • 25 Feb 2023 • Ziyue Liu, Yixing Li, Jing Hu, Xinling Yu, Shinyu Shiau, Xin Ai, Zhiyu Zeng, Zheng Zhang
In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations.
no code implementations • NeurIPS 2023 • Tao Ge, Jing Hu, Li Dong, Shaoguang Mao, Yan Xia, Xun Wang, Si-Qing Chen, Furu Wei
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL).
no code implementations • 10 Jul 2022 • Jie Gao, Jing Hu, Wanqing Sun, Yili Shen, Xiaonan Zhang, Xiaomin Fang, Fan Wang, Guodong Zhao
Our study highlights the prediction power of TCR and its potential value for cancer drug repurpose and precision oncology treatment.
no code implementations • 27 May 2022 • Xing Han, Tongzheng Ren, Jing Hu, Joydeep Ghosh, Nhat Ho
To attain this goal, each time series is first assigned the forecast for its cluster representative, which can be considered as a "shrinkage prior" for the set of time series it represents.
no code implementations • 22 Dec 2021 • Xing Han, Jing Hu, Joydeep Ghosh
We conduct a comprehensive evaluation of both point and quantile forecasts for hierarchical time series (HTS), including public data and user records from a large financial software company.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Siwei Lyu, Bin Kong, Youbing Yin, Qi Song, Xi Wu
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu, Siwei Lyu
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
no code implementations • 4 Dec 2021 • Jing Hu, Meiqi Zhang, Rui Zhang
In practical application, the performance of recognition network usually decreases when being applied on super-resolution images.
no code implementations • 29 Sep 2021 • Xing Han, Jing Hu, Joydeep Ghosh
We introduce a mixture of heterogeneous experts framework called MECATS, which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy.
no code implementations • 19 Sep 2020 • Jing Hu, Yueheng Lan
Synchronization is an important dynamical phenomenon in coupled nonlinear systems, which has been studied extensively in recent years.
Chaotic Dynamics
no code implementations • 29 Jan 2020 • Shanhui Sun, Jing Hu, Mingqing Yao, Jinrong Hu, Xiaodong Yang, Qi Song, Xi Wu
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.