no code implementations • 15 Mar 2024 • Yu Du, Yu Song, Ce Guo, Xiaojing Tian, Dong Liu, Ming Cong
Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry.
no code implementations • 21 Feb 2024 • Abhisek Chakraborty, Megan H. Murray, Ilya Lipkovich, Yu Du
The American Statistical Association (ASA) statement on statistical significance and P-values \cite{wasserstein2016asa} cautioned statisticians against making scientific decisions solely on the basis of traditional P-values.
1 code implementation • 6 Feb 2024 • Yu Du, Fangyun Wei, Hongyang Zhang
We also revisit the evaluation protocol introduced by previous works and identify a limitation in this protocol that leads to an artificially high pass rate.
no code implementations • 25 Jan 2024 • Tong Niu, Haoyu Huang, Yu Du, Weihao Zhang, Luping Shi, Rong Zhao
Given the escalating intricacy and multifaceted nature of contemporary social systems, manually generating solutions to address pertinent social issues has become a formidable task.
no code implementations • 7 Sep 2023 • Yu Du, Xu Liu, Yansong Chua
Speech enhancement seeks to extract clean speech from noisy signals.
1 code implementation • IEEE ROBOTICS AND AUTOMATION LETTERS 2023 • Weimin WANG, Ting Yang, Yu Du, Yu Liu
The proposed approach first constructs the CRF based on k-nearest neighbors with the snow confidence derived from the physical priors of snow, such as intensity and distribution.
no code implementations • 14 Aug 2023 • Shangxuan Li, Yu Du, Li Ye, Chichi Li, Yanshu Fang, Cheng Wang, Wu Zhou
In addition, there are weak edges at the tooth, between tooth and root canal, which makes it very difficult to segment such weak edges.
no code implementations • 27 Mar 2023 • Xu Liu, Mengyue Zhou, Gaosheng Shi, Yu Du, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Xintao Hu
Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models.
1 code implementation • 8 Jul 2022 • Yu Du, Miaojing Shi, Fangyun Wei, Guoqi Li
In this paper, we propose a new framework to boost ZSL by explicitly learning attribute prototypes beyond images and contrastively optimizing them with attribute-level features within images.
no code implementations • 20 May 2022 • Yuzhong Chen, Yu Du, Zhenxiang Xiao, Lin Zhao, Lu Zhang, David Weizhong Liu, Dajiang Zhu, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs).
1 code implementation • CVPR 2022 • Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, Guoqi Li
In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.
no code implementations • 1 Mar 2022 • Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, Yaguang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way.
2 code implementations • 20 Jan 2022 • Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, Yaguang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, Quoc Le
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #114 on Code Generation on HumanEval
no code implementations • 22 Jun 2021 • Yu Du
The results show that our model and algorithm can design the vehicle path superior to the original model in terms of distribution cost and delivery time, thus providing decision support for the M platform to save distribution cost in urban distribution in the future.
1 code implementation • 2 Jun 2021 • Mohammadreza Armandpour, Brian Kidd, Yu Du, Jianhua Z. Huang
To develop a better deep model for blood glucose forecasting, we analyze the data and detect important patterns.
no code implementations • 7 Aug 2020 • Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao
In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.