Search Results for author: Jing Hu

Found 16 papers, 4 papers with code

Efficient Image Super-Resolution via Symmetric Visual Attention Network

no code implementations17 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.

Image Super-Resolution

UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields

no code implementations10 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.

Computed Tomography (CT)

Controlling Neural Style Transfer with Deep Reinforcement Learning

no code implementations30 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.

reinforcement-learning Reinforcement Learning (RL) +1

Deep Reinforcement Learning for Image-to-Image Translation

1 code implementation24 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.

Auxiliary Learning Decision Making +3

In-context Autoencoder for Context Compression in a Large Language Model

1 code implementation13 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.

Language Modelling Large Language Model +3

DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design

no code implementations25 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.

Operator learning

TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response

no code implementations10 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.

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

no code implementations27 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.

Clustering Time Series +1

Dynamic Combination of Heterogeneous Models for Hierarchical Time Series

no code implementations22 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.

Time Series Time Series Analysis

Stochastic Actor-Executor-Critic for Image-to-Image Translation

1 code implementation14 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.

Continuous Control Image-to-Image Translation +3

Feature-based Recognition Framework for Super-resolution Images

no code implementations4 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.

Super-Resolution

MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series

no code implementations29 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.

Time Series Time Series Analysis

Koopman analysis in oscillator synchronization

no code implementations19 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

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