no code implementations • EMNLP 2020 • Lijie Wang, Ao Zhang, Kun Wu, Ke Sun, Zhenghua Li, Hua Wu, Min Zhang, Haifeng Wang
This paper describes in detail the construction process and data statistics of DuSQL.
1 code implementation • 23 Feb 2024 • Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Kun Wu, Nishant Ravikumar, Alejandro F. Frangi
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.
no code implementations • 20 Feb 2024 • Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei
The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks.
no code implementations • 17 Jan 2024 • Yinuo Zhao, Kun Wu, Tianjiao Yi, Zhiyuan Xu, Xiaozhu Ju, Zhengping Che, Qinru Qiu, Chi Harold Liu, Jian Tang
Visuomotor policies, which learn control mechanisms directly from high-dimensional visual observations, confront challenges in adapting to new environments with intricate visual variations.
no code implementations • 17 Jan 2024 • Kun Wu, Ning Liu, Zhen Zhao, Di Qiu, Jinming Li, Zhengping Che, Zhiyuan Xu, Qinru Qiu, Jian Tang
Imitation learning (IL), aiming to learn optimal control policies from expert demonstrations, has been an effective method for robot manipulation tasks.
no code implementations • 20 Dec 2023 • Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Jian Tang
Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature.
no code implementations • 20 Dec 2023 • Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Qinru Qiu, Jian Tang
Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i. e., the spatio-temporal video content and the word sequence in question.
no code implementations • 16 Jan 2023 • Kun Wu, Mert Hidayetoğlu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-mei Hwu
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs.
no code implementations • 10 Jul 2022 • Kun Wu, Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang, Dejun Yang
In this paper, we define a new problem called continual few-shot learning, in which tasks arrive sequentially and each task is associated with a few training samples.
1 code implementation • 27 Jun 2022 • Jun Li, Yushan Zheng, Kun Wu, Jun Shi, Fengying Xie, Zhiguo Jiang
In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis.
no code implementations • 26 Apr 2022 • Kun Wu, Lijie Wang, Zhenghua Li, Xinyan Xiao
Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries.
1 code implementation • 17 Feb 2022 • Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, Chi Harold Liu
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors.
no code implementations • 10 Nov 2021 • Seung Won Min, Kun Wu, Mert Hidayetoğlu, JinJun Xiong, Xiang Song, Wen-mei Hwu
With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead.
no code implementations • 23 Jul 2021 • Kun Wu, Chengxiang Yin, Zhengping Che, Bo Jiang, Jian Tang, Zheng Guan, Gangyi Ding
Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others.
1 code implementation • 4 Mar 2021 • Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu
In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help.
1 code implementation • EMNLP 2021 • Kun Wu, Lijie Wang, Zhenghua Li, Ao Zhang, Xinyan Xiao, Hua Wu, Min Zhang, Haifeng Wang
For better distribution matching, we require that at least 80% of SQL patterns in the training data are covered by generated queries.
1 code implementation • 20 Jan 2021 • Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu
While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step.
no code implementations • ICCV 2021 • Chengxiang Yin, Kun Wu, Zhengping Che, Bo Jiang, Zhiyuan Xu, Jian Tang
Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data.
1 code implementation • 28 Dec 2020 • Carl Pearson, Kun Wu, I-Hsin Chung, JinJun Xiong, Wen-mei Hwu
MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications.
Distributed, Parallel, and Cluster Computing
1 code implementation • NeurIPS 2020 • Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks.
1 code implementation • ICLR 2018 • Runyao Chen, Kun Wu, Ping Luo
Mini-batch gradient descent and its variants are commonly used in deep learning.