1 code implementation • 25 Mar 2024 • Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
no code implementations • 16 May 2023 • Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun
Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities.
no code implementations • 29 Mar 2023 • Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, Bin Dong
(4) Can ChatGPT effectively leverage commonsense for answering questions?
no code implementations • 27 Mar 2023 • Yiqing Shen, Pengfei Guo, Jingpu Wu, Qianqi Huang, Nhat Le, Jinyuan Zhou, Shanshan Jiang, Mathias Unberath
We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available.
no code implementations • 9 Feb 2022 • Sheng-Guo Wang, Shanshan Jiang
Recently, Machine Learning (ML), Artificial Intelligence (AI), and Convolutional Neural Network (CNN) have made huge progress with broad applications, where their systems have deep learning structures and a large number of hyperparameters that determine the quality and performance of the CNNs and AI systems.
1 code implementation • 23 Jan 2022 • Pengfei Guo, Yiqun Mei, Jinyuan Zhou, Shanshan Jiang, Vishal M. Patel
Accelerating magnetic resonance image (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space.
no code implementations • 16 Jun 2021 • Pengfei Guo, Jeya Maria Jose Valanarasu, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, Vishal M. Patel
Reconstructing magnetic resonance (MR) images from undersampled data is a challenging problem due to various artifacts introduced by the under-sampling operation.
1 code implementation • 18 May 2021 • Yuanming Li, Huaizheng Zhang, Shanshan Jiang, Fan Yang, Yonggang Wen, Yong Luo
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background.
1 code implementation • CVPR 2021 • Pengfei Guo, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, Vishal M. Patel
However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc.
1 code implementation • 6 Aug 2020 • Pengfei Guo, Puyang Wang, Rajeev Yasarla, Jinyuan Zhou, Vishal M. Patel, Shanshan Jiang
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images.
1 code implementation • 26 Jun 2020 • Pengfei Guo, Puyang Wang, Jinyuan Zhou, Vishal M. Patel, Shanshan Jiang
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional and advanced molecular MR images.
no code implementations • IJCNLP 2019 • Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang
Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck.
no code implementations • WS 2019 • Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, Bin Dong
This is the second time for SRCB to participate in WAT.
no code implementations • 9 May 2018 • Xi Zhang, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, Gady Agam
We show that by using masks the motion estimate results in a quadratic function of input features in the output layer.
1 code implementation • 21 Jul 2016 • Xi Zhang, Di Ma, Lin Gan, Shanshan Jiang, Gady Agam
In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance.
no code implementations • 17 Sep 2015 • Xi Zhang, Yanwei Fu, Shanshan Jiang, Leonid Sigal, Gady Agam
In this paper, we investigate and formalize a general framework-Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently.