In February 2023, the "Notice on Recommending Papers for the "2023 World Artificial Intelligence Conference Youth Excellent Paper Award"" was released, and the collection of excellent papers for young people in the field of artificial intelligence was launched for universities, research institutes, and enterprises around the world. By the end of the call for papers, a total of 235 papers from home and abroad have been received, including internationally renowned universities, scientific research institutions, and enterprises.
After the initial evaluation, re-evaluation, and final evaluation, 10 papers for the 2023 World Artificial Intelligence Conference Outstanding Youth Paper Award and 10 papers for the nomination award were finally selected. The announcement is as follows:
2023 World Artificial Intelligence Conference Outstanding Youth Paper Award
(According to the initial alphabetical order of the English title of the papers, there are 10 papers in total)
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ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing, Yan Yan, Xi'an Jiaotong University, IEEE Transactions on Pattern Analysis and Machine Intelligence 2020
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Finding key players in complex networks through deep reinforcement learning, Changjun Fan, National University of Defense Technology, Nature Machine Intelligence 2020
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Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning, Zhang Yunwei, Chuoqiao University, Nature Communications 2020
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Parameter-efficient fine-tuning of large-scale pre-trained language models, Ding Ning, Tsinghua University, Nature Machine Intelligence 2023
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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions, Wenhai Wang, Nanjing University, IEEE/CVF International Conference on Computer Vision (ICCV2021)
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Quantum computational advantage using photons, Zhong Hansen, University of Science and Technology of China, Science 2020
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Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers，郑思晓，复旦大学，IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2021)
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SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment, Zhiyuan Yuan, Tsinghua University, Nature Methods2021
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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers，谢恩泽，香港大学，Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
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Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications, Jin Tao, Shanghai University, National University of Singapore, Nature Communications2020
2023 World Artificial Intelligence Conference Youth Excellent Paper Nomination Award
(According to the initial alphabetical order of the English title of the papers, there are 10 papers in total)
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CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP, Qin Libo, Harbin Institute of Technology, International Joint Conference on Artificial Intelligence (IJCAI2020)
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DropMessage: Unifying Random Dropping for Graph Neural Networks, Fang Taoran, Zhejiang University, AAAI-23
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EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation, Chen Hansheng, Tongji University, Alibaba Dharma Lab, IEEE/CVF Computer Vision and Pattern Recognition Conference2022
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Lucid: A Non-Intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs, Qinghao Hu, Shanghai Artificial Intelligence Laboratory, Nanyang Technological University, Singapore, (ASPLOS '23) The 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
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Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans，彭思达，浙江大学，IEEE/CVF Conference on Computer Vision and Pattern Recognition2021
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Perseus: A Fail-Slow Detection Framework for Cloud Storage Systems, Ruiming Lu, Shanghai Jiaotong University, Alibaba, USENIX Conference on File and Storage Technologies (FAST2023)
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Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning, 钱学骏, Southern California University of Technology, Nature Biomedical Engineering2021
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RepVGG: Making VGG-style ConvNets Great Again, Ding Xiaohan, Tsinghua University, CVPR 2021
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Simple and Deep Graph Convolutional Networks, Ming Chen, Renmin University of China, International Conference on Machine Learning2020
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Towards Robust Blind Face Restoration with Codebook Lookup Transformer, Zhou Shangchen, Nanyang Technological University, Singapore, Neural Information Processing Systems (NeurIPS2022)