Search Results for author: Yu Shen

Found 18 papers, 7 papers with code

GAN-based Garment Generation Using Sewing Pattern Images

no code implementations ECCV 2020 Yu Shen, Junbang Liang, Ming C. Lin

The generation of realistic apparel model has become increasingly popular as a result of the rapid pace of change in fashion trends and the growing need for garment models in various applications such as virtual try-on.

Virtual Try-on

Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

1 code implementation31 Jul 2021 Wentao Zhang, Zhi Yang, Yexin Wang, Yu Shen, Yang Li, Liang Wang, Bin Cui

Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.

Active Learning Knowledge Graphs

ROD: Reception-aware Online Distillation for Sparse Graphs

1 code implementation25 Jul 2021 Wentao Zhang, Yuezihan Jiang, Yang Li, Zeang Sheng, Yu Shen, Xupeng Miao, Liang Wang, Zhi Yang, Bin Cui

Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.

Graph Learning Knowledge Distillation +4

VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

3 code implementations19 Jul 2021 Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui

End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.

AutoML Feature Engineering +1

OpenBox: A Generalized Black-box Optimization Service

6 code implementations1 Jun 2021 Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.

Experimental Design Transfer Learning

Secure Artificial Intelligence of Things for Implicit Group Recommendations

no code implementations23 Apr 2021 Keping Yu, Zhiwei Guo, Yu Shen, Wei Wang, Jerry Chun-Wei Lin, Takuro Sato

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications such as group recommender systems.

Recommendation Systems

GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing

no code implementations20 Apr 2021 Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui

In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images

no code implementations26 Feb 2021 Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +1

Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples

no code implementations1 Jan 2021 Yu Shen, Laura Yu Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin

To ensure the wide adoption and safety of autonomous driving, the vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +2

A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data

no code implementations9 Dec 2020 Bing Liu, Yu Tang, Yuxiong Ji, Yu Shen, Yuchuan Du

Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway.

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

6 code implementations5 Dec 2020 Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui

Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.

Hyperparameter Optimization

Design of phase III trials with long-term survival outcomes based on short-term binary results

1 code implementation29 Aug 2020 Marta Bofill Roig, Yu Shen, Guadalupe Gómez Melis

We propose to base the comparison between arms on the difference of the restricted mean survival times, and show how the effect size and sample size for overall survival rely on the probability of the binary response and the survival distribution by response status, both for each treatment arm.

Methodology Applications

Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification

1 code implementation2 Aug 2020 Yu Shen, Sijie Zhu, Chen Chen, Qian Du, Liang Xiao, Jianyu Chen, Delu Pan

Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.

Classification General Classification +1

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