Search Results for author: Lingyu Zhang

Found 14 papers, 5 papers with code

Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation

no code implementations13 Jan 2023 Hongjun Wang, Zhiwen Zhang, Zipei Fan, Jiyuan Chen, Lingyu Zhang, Ryosuke Shibasaki, Xuan Song

Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.

Adversarially Robust Video Perception by Seeing Motion

no code implementations13 Dec 2022 Lingyu Zhang, Chengzhi Mao, Junfeng Yang, Carl Vondrick

Even under adaptive attacks where the adversary knows our defense, our algorithm is still effective.

Adversarial Robustness

Robust Perception through Equivariance

no code implementations12 Dec 2022 Chengzhi Mao, Lingyu Zhang, Abhishek Joshi, Junfeng Yang, Hao Wang, Carl Vondrick

In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference.

Adversarial Robustness Instance Segmentation +1

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

1 code implementation28 Nov 2022 Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Boyuan Zhang, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Xuan Song

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.

Research on the Inverse Kinematics Prediction of a Soft Biomimetic Actuator via BP Neural Network

no code implementations26 Oct 2021 Huichen Ma, Junjie Zhou, Jian Zhang, Lingyu Zhang

After training with sample data, the BP neural network model can represent the relation between the manipulator tip position and the pressure applied to the chambers.

Motion Planning

Apply Artificial Neural Network to Solving Manpower Scheduling Problem

1 code implementation7 May 2021 Tianyu Liu, Lingyu Zhang

This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem based on the existing research.

Scheduling Time Series

An Intelligent Model for Solving Manpower Scheduling Problems

1 code implementation7 May 2021 Lingyu Zhang, Tianyu Liu, Yunhai Wang

In addition, to the numerical solution of the manpower scheduling problem, this paper also studies the algorithm for scheduling task list generation and the method of displaying scheduling results.

Management Scheduling

Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy

no code implementations7 Aug 2020 Teng Ye, Wei Ai, Lingyu Zhang, Ning Luo, Lulu Zhang, Jieping Ye, Qiaozhu Mei

Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.

CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction

no code implementations25 Sep 2019 Xu Geng, Lingyu Zhang, Shulin Li, Yuanbo Zhang, Lulu Zhang, Leye Wang, Qiang Yang, Hongtu Zhu, Jieping Ye

Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.

Graph Attention Spatio-Temporal Forecasting +1

Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization

no code implementations ACL 2019 Manling Li, Lingyu Zhang, Heng Ji, Richard J. Radke

Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.

Meeting Summarization Text Generation

Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

no code implementations27 May 2019 Xu Geng, Xiyu Wu, Lingyu Zhang, Qiang Yang, Yan Liu, Jieping Ye

To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.

BIG-bench Machine Learning

POI Semantic Model with a Deep Convolutional Structure

no code implementations18 Mar 2019 Ji Zhao, Meiyu Yu, Huan Chen, Boning Li, Lingyu Zhang, Qi Song, Li Ma, Hua Chai, Jieping Ye

An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.


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