Search Results for author: Yu Shen

Found 37 papers, 14 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.

Garment Reconstruction Virtual Try-on

SHARE: Single-view Human Adversarial REconstruction

no code implementations30 Dec 2023 Shreelekha Revankar, Shijia Liao, Yu Shen, Junbang Liang, Huaishu Peng, Ming Lin

We perform a comprehensive analysis on the impact of camera poses on HPS reconstruction outcomes.

Data Augmentation

Towards General and Efficient Online Tuning for Spark

no code implementations5 Sep 2023 Yang Li, Huaijun Jiang, Yu Shen, Yide Fang, Xiaofeng Yang, Danqing Huang, Xinyi Zhang, Wentao Zhang, Ce Zhang, Peng Chen, Bin Cui

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.

Bayesian Optimization Meta-Learning

OpenBox: A Python Toolkit for Generalized Black-box Optimization

1 code implementation26 Apr 2023 Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui

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

Experimental Design

Transfer Learning for Bayesian Optimization: A Survey

no code implementations12 Feb 2023 Tianyi Bai, Yang Li, Yu Shen, Xinyi Zhang, Wentao Zhang, Bin Cui

A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.

Bayesian Optimization Transfer Learning

Rover: An online Spark SQL tuning service via generalized transfer learning

no code implementations8 Feb 2023 Yu Shen, Xinyuyang Ren, Yupeng Lu, Huaijun Jiang, Huanyong Xu, Di Peng, Yang Li, Wentao Zhang, Bin Cui

When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.

Bayesian Optimization Transfer Learning

DivBO: Diversity-aware CASH for Ensemble Learning

no code implementations7 Feb 2023 Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, Bin Cui

To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems.

AutoML Bayesian Optimization +1

Efficient End-to-End AutoML via Scalable Search Space Decomposition

1 code implementation19 Jun 2022 Yang Li, Yu Shen, Wentao Zhang, 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

NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

2 code implementations17 Jun 2022 Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui

First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.

Graph Representation Learning Link Prediction +1

Transfer Learning based Search Space Design for Hyperparameter Tuning

no code implementations6 Jun 2022 Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui

The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.

Bayesian Optimization BIG-bench Machine Learning +2

TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning

no code implementations6 Jun 2022 Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui

With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.

Hyperparameter Optimization Neural Architecture Search +2

AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning

no code implementations22 May 2022 Michael Villarreal, Bibek Poudel, Ryan Wickman, Yu Shen, Weizi Li

As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many 'perception-to-control' systems are developed and deployed.

Denoising

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

no code implementations7 Apr 2022 Zhiyan Chen, Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein T. Mouftah, Petar Djukic

Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics.

Intrusion Detection

PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm

1 code implementation1 Mar 2022 Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui

Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.

Neural Architecture Search

Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale

no code implementations18 Jan 2022 Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui

The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.

Scheduling

Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios

no code implementations17 Dec 2021 Yiyue Zhao, Cailin Lei, Yu Shen, Yuchuan Du, Qijun Chen

To enhance the visual perception capability of human-vehicle cooperative driving, this paper proposed a cooperative visual perception model.

Autonomous Driving object-detection +2

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering

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

We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving.

Autonomous Driving Self-Driving Cars

Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup

1 code implementation31 Oct 2021 Huaijun Jiang, Yu Shen, Yang Li

In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2).

Bayesian Optimization Hyperparameter Optimization

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.

Clustering Graph Learning +5

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.

Management 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.

Reinforcement Learning (RL)

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

5 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.

Bayesian Optimization 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.

General Classification Hyperspectral Image Classification

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