Search Results for author: Wei Shao

Found 47 papers, 14 papers with code

Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies

1 code implementation23 Jan 2024 Lincan Li, Wei Shao, Wei Dong, Yijun Tian, Qiming Zhang, Kaixiang Yang, Wenjie Zhang

There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.

Autonomous Driving

Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them

no code implementations22 Jan 2024 Chao Liu, Boxi Chen, Wei Shao, Chris Zhang, Kelvin Wong, Yi Zhang

Through our comprehensive review and analysis, this paper seeks to contribute to the ongoing discourse on ML-based IoT security, offering valuable insights and practical solutions to secure ML models and data in the rapidly expanding field of artificial intelligence in IoT.

Anomaly Detection Model extraction

DistillCSE: Distilled Contrastive Learning for Sentence Embeddings

1 code implementation20 Oct 2023 Jiahao Xu, Wei Shao, Lihui Chen, Lemao Liu

This paper proposes the DistillCSE framework, which performs contrastive learning under the self-training paradigm with knowledge distillation.

Contrastive Learning Knowledge Distillation +2

Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

1 code implementation21 Jul 2023 Qingyue Wei, Lequan Yu, Xianhang Li, Wei Shao, Cihang Xie, Lei Xing, Yuyin Zhou

Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.

Image Segmentation Meta-Learning +4

MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images

1 code implementation31 May 2023 Hongxu Jiang, Muhammad Imran, Preethika Muralidharan, Anjali Patel, Jake Pensa, Muxuan Liang, Tarik Benidir, Joseph R. Grajo, Jason P. Joseph, Russell Terry, John Michael DiBianco, Li-Ming Su, Yuyin Zhou, Wayne G. Brisbane, Wei Shao

During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations.

Segmentation

SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives

no code implementations22 May 2023 Jiahao Xu, Wei Shao, Lihui Chen, Lemao Liu

This paper improves contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption.

Contrastive Learning Sentence +1

Message Passing Neural Networks for Traffic Forecasting

no code implementations9 May 2023 Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim

A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location.

Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)

1 code implementation9 May 2023 Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim

During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training.

Contrastive Learning

CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection

no code implementations23 Apr 2023 Zhongyu Yang, Chen Shen, Wei Shao, Tengfei Xing, Runbo Hu, Pengfei Xu, Hua Chai, Ruini Xue

A lane instance is first responded by the heat-map on the U-shaped curved guide line at global semantic level, thus the corresponding features of each lane are aggregated at the response point.

 Ranked #1 on Lane Detection on CurveLanes (Recall metric)

Lane Detection

CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

no code implementations9 Feb 2023 Sheng Yue, Guanbo Wang, Wei Shao, Zhaofeng Zhang, Sen Lin, Ju Ren, Junshan Zhang

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift.

Continuous Control reinforcement-learning +1

Biomedical image analysis competitions: The state of current participation practice

no code implementations16 Dec 2022 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, Alison Noble, Carlos Ortiz de Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne Martel, Carlos Martín-Isla, Bjoern Menze, Chinedu Innocent Nwoye, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Carole Sudre, Kimberlin Van Wijnen, Armine Vardazaryan, Tom Vercauteren, Martin Wagner, Chuanbo Wang, Moi Hoon Yap, Zeyun Yu, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Rina Bao, Chanyeol Choi, Andrew Cohen, Oleh Dzyubachyk, Adrian Galdran, Tianyuan Gan, Tianqi Guo, Pradyumna Gupta, Mahmood Haithami, Edward Ho, Ikbeom Jang, Zhili Li, Zhengbo Luo, Filip Lux, Sokratis Makrogiannis, Dominik Müller, Young-tack Oh, Subeen Pang, Constantin Pape, Gorkem Polat, Charlotte Rosalie Reed, Kanghyun Ryu, Tim Scherr, Vajira Thambawita, Haoyu Wang, Xinliang Wang, Kele Xu, Hung Yeh, Doyeob Yeo, Yixuan Yuan, Yan Zeng, Xin Zhao, Julian Abbing, Jannes Adam, Nagesh Adluru, Niklas Agethen, Salman Ahmed, Yasmina Al Khalil, Mireia Alenyà, Esa Alhoniemi, Chengyang An, Talha Anwar, Tewodros Weldebirhan Arega, Netanell Avisdris, Dogu Baran Aydogan, Yingbin Bai, Maria Baldeon Calisto, Berke Doga Basaran, Marcel Beetz, Cheng Bian, Hao Bian, Kevin Blansit, Louise Bloch, Robert Bohnsack, Sara Bosticardo, Jack Breen, Mikael Brudfors, Raphael Brüngel, Mariano Cabezas, Alberto Cacciola, Zhiwei Chen, Yucong Chen, Daniel Tianming Chen, Minjeong Cho, Min-Kook Choi, Chuantao Xie Chuantao Xie, Dana Cobzas, Julien Cohen-Adad, Jorge Corral Acero, Sujit Kumar Das, Marcela de Oliveira, Hanqiu Deng, Guiming Dong, Lars Doorenbos, Cory Efird, Sergio Escalera, Di Fan, Mehdi Fatan Serj, Alexandre Fenneteau, Lucas Fidon, Patryk Filipiak, René Finzel, Nuno R. Freitas, Christoph M. Friedrich, Mitchell Fulton, Finn Gaida, Francesco Galati, Christoforos Galazis, Chang Hee Gan, Zheyao Gao, Shengbo Gao, Matej Gazda, Beerend Gerats, Neil Getty, Adam Gibicar, Ryan Gifford, Sajan Gohil, Maria Grammatikopoulou, Daniel Grzech, Orhun Güley, Timo Günnemann, Chunxu Guo, Sylvain Guy, Heonjin Ha, Luyi Han, Il Song Han, Ali Hatamizadeh, Tian He, Jimin Heo, Sebastian Hitziger, SeulGi Hong, Seungbum Hong, Rian Huang, Ziyan Huang, Markus Huellebrand, Stephan Huschauer, Mustaffa Hussain, Tomoo Inubushi, Ece Isik Polat, Mojtaba Jafaritadi, SeongHun Jeong, Bailiang Jian, Yuanhong Jiang, Zhifan Jiang, Yueming Jin, Smriti Joshi, Abdolrahim Kadkhodamohammadi, Reda Abdellah Kamraoui, Inha Kang, Junghwa Kang, Davood Karimi, April Khademi, Muhammad Irfan Khan, Suleiman A. Khan, Rishab Khantwal, Kwang-Ju Kim, Timothy Kline, Satoshi Kondo, Elina Kontio, Adrian Krenzer, Artem Kroviakov, Hugo Kuijf, Satyadwyoom Kumar, Francesco La Rosa, Abhi Lad, Doohee Lee, Minho Lee, Chiara Lena, Hao Li, Ling Li, Xingyu Li, Fuyuan Liao, Kuanlun Liao, Arlindo Limede Oliveira, Chaonan Lin, Shan Lin, Akis Linardos, Marius George Linguraru, Han Liu, Tao Liu, Di Liu, Yanling Liu, João Lourenço-Silva, Jingpei Lu, Jiangshan Lu, Imanol Luengo, Christina B. Lund, Huan Minh Luu, Yi Lv, Uzay Macar, Leon Maechler, Sina Mansour L., Kenji Marshall, Moona Mazher, Richard McKinley, Alfonso Medela, Felix Meissen, Mingyuan Meng, Dylan Miller, Seyed Hossein Mirjahanmardi, Arnab Mishra, Samir Mitha, Hassan Mohy-ud-Din, Tony Chi Wing Mok, Gowtham Krishnan Murugesan, Enamundram Naga Karthik, Sahil Nalawade, Jakub Nalepa, Mohamed Naser, Ramin Nateghi, Hammad Naveed, Quang-Minh Nguyen, Cuong Nguyen Quoc, Brennan Nichyporuk, Bruno Oliveira, David Owen, Jimut Bahan Pal, Junwen Pan, Wentao Pan, Winnie Pang, Bogyu Park, Vivek Pawar, Kamlesh Pawar, Michael Peven, Lena Philipp, Tomasz Pieciak, Szymon Plotka, Marcel Plutat, Fattaneh Pourakpour, Domen Preložnik, Kumaradevan Punithakumar, Abdul Qayyum, Sandro Queirós, Arman Rahmim, Salar Razavi, Jintao Ren, Mina Rezaei, Jonathan Adam Rico, ZunHyan Rieu, Markus Rink, Johannes Roth, Yusely Ruiz-Gonzalez, Numan Saeed, Anindo Saha, Mostafa Salem, Ricardo Sanchez-Matilla, Kurt Schilling, Wei Shao, Zhiqiang Shen, Ruize Shi, Pengcheng Shi, Daniel Sobotka, Théodore Soulier, Bella Specktor Fadida, Danail Stoyanov, Timothy Sum Hon Mun, Xiaowu Sun, Rong Tao, Franz Thaler, Antoine Théberge, Felix Thielke, Helena Torres, Kareem A. Wahid, Jiacheng Wang, Yifei Wang, Wei Wang, Xiong Wang, Jianhui Wen, Ning Wen, Marek Wodzinski, Ye Wu, Fangfang Xia, Tianqi Xiang, Chen Xiaofei, Lizhan Xu, Tingting Xue, Yuxuan Yang, Lin Yang, Kai Yao, Huifeng Yao, Amirsaeed Yazdani, Michael Yip, Hwanseung Yoo, Fereshteh Yousefirizi, Shunkai Yu, Lei Yu, Jonathan Zamora, Ramy Ashraf Zeineldin, Dewen Zeng, Jianpeng Zhang, Bokai Zhang, Jiapeng Zhang, Fan Zhang, Huahong Zhang, Zhongchen Zhao, Zixuan Zhao, Jiachen Zhao, Can Zhao, Qingshuo Zheng, Yuheng Zhi, Ziqi Zhou, Baosheng Zou, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

Of these, 84% were based on standard architectures.

Benchmarking

Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems

no code implementations23 Nov 2022 Jirong Yi, Qiaosheng Zhang, Zhen Chen, Qiao Liu, Wei Shao, Yusen He, Yaohua Wang

We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique.

regression

Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

no code implementations11 Nov 2022 Yuxi Liu, Shaowen Qin, Antonio Jimeno Yepes, Wei Shao, Zhenhao Zhang, Flora D. Salim

Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.

Decision Making Imputation

Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems

no code implementations3 Oct 2022 Jirong Yi, Qiaosheng Zhang, Zhen Chen, Qiao Liu, Wei Shao

Deep learning systems have been reported to acheive state-of-the-art performances in many applications, and one of the keys for achieving this is the existence of well trained classifiers on benchmark datasets which can be used as backbone feature extractors in downstream tasks.

Binary Classification Data Augmentation

Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems

no code implementations21 Sep 2022 Jirong Yi, Qiaosheng Zhang, Zhen Chen, Qiao Liu, Wei Shao

Deep learning systems have been reported to achieve state-of-the-art performances in many applications, and a key is the existence of well trained classifiers on benchmark datasets.

Binary Classification

Compound Density Networks for Risk Prediction using Electronic Health Records

no code implementations2 Aug 2022 Yuxi Liu, Shaowen Qin, Zhenhao Zhang, Wei Shao

We propose an integrated end-to-end approach by utilizing a Compound Density Network (CDNet) that allows the imputation method and prediction model to be tuned together within a single framework.

Imputation Mortality Prediction

How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies

1 code implementation11 Jul 2022 Edward Small, Wei Shao, Zeliang Zhang, Peihan Liu, Jeffrey Chan, Kacper Sokol, Flora Salim

Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.

Decision Making Fairness +1

CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

2 code implementations5 Jul 2022 Runsheng Xu, Zhengzhong Tu, Hao Xiang, Wei Shao, Bolei Zhou, Jiaqi Ma

The extensive experiments on the V2V perception dataset, OPV2V, demonstrate that CoBEVT achieves state-of-the-art performance for cooperative BEV semantic segmentation.

3D Object Detection Autonomous Driving +3

Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models

no code implementations16 Jun 2022 Wei Shao, Lei Huang, Shuqi Liu, Shihua Ma, Linqi Song

In this paper, we propose an embedding regularized neural topic model, which applies the specially designed training constraints on word embedding and topic embedding to reduce the optimization space of parameters.

Topic Models

Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

1 code implementation23 Apr 2022 Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim

To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.

Graph Attention Spatio-Temporal Forecasting

Measuring disentangled generative spatio-temporal representation

no code implementations10 Feb 2022 Sichen Zhao, Wei Shao, Jeffrey Chan, Flora D. Salim

Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.

Dimensionality Reduction Representation Learning

Bridging the gap between prostate radiology and pathology through machine learning

no code implementations3 Dec 2021 Indrani Bhattacharya, David S. Lim, Han Lin Aung, Xingchen Liu, Arun Seetharaman, Christian A. Kunder, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Pejman Ghanouni, Katherine J. To'o, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu

Our experiments show that (1) radiologist labels and models trained with them can miss cancers, or underestimate cancer extent, (2) digital pathologist labels and models trained with them have high concordance with pathologist labels, and (3) models trained with digital pathologist labels achieve the best performance in prostate cancer detection in two different cohorts with different disease distributions, irrespective of the model architecture used.

BIG-bench Machine Learning

Spatio-temporal Disentangled representation learning for mobility prediction

no code implementations29 Sep 2021 Sichen Zhao, Wei Shao, Jeffrey Chan, Flora D. Salim

In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.

Management Representation Learning

Weakly Supervised Registration of Prostate MRI and Histopathology Images

no code implementations23 Jun 2021 Wei Shao, Indrani Bhattacharya, Simon J. C. Soerensen, Christian A. Kunder, Jeffrey B. Wang, Richard E. Fan, Pejman Ghanouni, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu

Cancer labels achieved by image registration can be used to improve radiologists' interpretation of MRI by training deep learning models for early detection of prostate cancer.

Image Registration

Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts

1 code implementation12 Jun 2021 Wei Shao, Yue Pan, Oguz C. Durumeric, Joseph M. Reinhardt, John E. Bayouth, Mirabela Rusu, Gary E. Christensen

Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression.

regression Time Series Analysis

MoParkeR : Multi-objective Parking Recommendation

no code implementations10 Jun 2021 Mohammad Saiedur Rahaman, Wei Shao, Flora D. Salim, Ayad Turky, Andy Song, Jeffrey Chan, Junliang Jiang, Doug Bradbrook

Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.

Recommendation Systems

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

no code implementations19 May 2021 Wei Shao, Arian Prabowo, Sichen Zhao, Piotr Koniusz, Flora D. Salim

To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas.

Generative Adversarial Networks for Spatio-temporal Data: A Survey

no code implementations18 Aug 2020 Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim

Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.

Imputation Time Series +2

Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters

no code implementations9 Aug 2020 Zhi Huang, Paul Salama, Wei Shao, Jie Zhang, Kun Huang

Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.

regression

G-CREWE: Graph CompREssion With Embedding for Network Alignment

1 code implementation30 Jul 2020 Kyle K. Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra

In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem.

BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis

1 code implementation31 May 2020 Wei Li, Wei Shao, Shaoxiong Ji, Erik Cambria

Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.

Emotion Recognition in Conversation Sentence +1

Transfer Learning for Thermal Comfort Prediction in Multiple Cities

no code implementations29 Apr 2020 Nan Gao, Wei Shao, Mohammad Saiedur Rahaman, Jun Zhai, Klaus David, Flora D. Salim

The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage.

Transfer Learning

Towards Fair Cross-Domain Adaptation via Generative Learning

no code implementations4 Mar 2020 Tongxin Wang, Zhengming Ding, Wei Shao, Haixu Tang, Kun Huang

Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions.

Domain Adaptation domain classification +1

COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

no code implementations24 Sep 2019 Arian Prabowo, Piotr Koniusz, Wei Shao, Flora D. Salim

This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments.

Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network

no code implementations8 Mar 2019 Wei Shao, Flora D. Salim, Jeffrey Chan, Sean Morrison, Fabio Zambetta

Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques.

General Classification Test

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