1 code implementation • 15 Apr 2024 • Hang Gao, Yongfeng Zhang
In the realm of artificial intelligence, the adaptation of Large Language Model (LLM)-based agents to execute tasks via natural language prompts represents a significant advancement, notably eliminating the need for explicit retraining or fine tuning for fixed-answer tasks such as common sense questions and yes/no queries.
no code implementations • 18 Mar 2024 • Hang Gao, Jiaguo Yuan, Jiangmeng Li, Chengyu Yao, Fengge Wu, Junsuo Zhao, Changwen Zheng
PLL is a critical weakly supervised learning problem, where each training instance is associated with a set of candidate labels, including both the true label and additional noisy labels.
no code implementations • 16 Feb 2024 • Zhen Zhang, Yuhua Zhao, Hang Gao, Mengting Hu
Named Entity Recognition (NER) serves as a fundamental task in natural language understanding, bearing direct implications for web content analysis, search engines, and information retrieval systems.
1 code implementation • 21 Dec 2023 • Jiangmeng Li, Yifan Jin, Hang Gao, Wenwen Qiang, Changwen Zheng, Fuchun Sun
To this end, we propose a novel hierarchical topology isomorphism expertise embedded graph contrastive learning, which introduces knowledge distillations to empower GCL models to learn the hierarchical topology isomorphism expertise, including the graph-tier and subgraph-tier.
1 code implementation • 15 Dec 2023 • Hang Gao, Chengyu Yao, Jiangmeng Li, Lingyu Si, Yifan Jin, Fengge Wu, Changwen Zheng, Huaping Liu
In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels.
1 code implementation • 8 Dec 2023 • Yuanyuan Guo, Zehua Zang, Hang Gao, Xiao Xu, Rui Wang, Lixiang Liu, Jiangmeng Li
To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner.
no code implementations • 17 Oct 2023 • Xianyue Peng, Hang Gao, Hao Wang, H. Michael Zhang
Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way.
no code implementations • 16 Oct 2023 • Xianyue Peng, Hang Gao, Gengyue Han, Hao Wang, Michael Zhang
In this paper, we propose a joint optimization approach for traffic signal control and vehicle routing in signalized road networks.
no code implementations • 29 Sep 2023 • Zhen Liu, Hang Gao, Hao Ma, Shuo Cai, Yunfeng Hu, Ting Qu, Hong Chen, Xun Gong
Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia.
no code implementations • 21 Aug 2023 • Jiangmeng Li, Hang Gao, Wenwen Qiang, Changwen Zheng
To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning.
1 code implementation • 1 Jun 2023 • Mengting Hu, Yinhao Bai, Yike Wu, Zhen Zhang, Liqi Zhang, Hang Gao, Shiwan Zhao, Minlie Huang
We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens.
no code implementations • ICCV 2023 • RuiLong Li, Hang Gao, Matthew Tancik, Angjoo Kanazawa
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering.
no code implementations • 20 Jan 2023 • Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Xingzhe Su, Fengge Wu, Changwen Zheng, Fuchun Sun
By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance.
1 code implementation • 24 Oct 2022 • Hang Gao, RuiLong Li, Shubham Tulsiani, Bryan Russell, Angjoo Kanazawa
We study the recent progress on dynamic view synthesis (DVS) from monocular video.
1 code implementation • 19 Oct 2022 • Mengting Hu, Yike Wu, Hang Gao, Yinhao Bai, Shiwan Zhao
By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.
Ranked #3 on Aspect-Based Sentiment Analysis (ABSA) on ACOS
1 code implementation • COLING 2022 • Mengting Hu, Hang Gao, Yinhao Bai, Mingming Liu
Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers.
1 code implementation • 18 Aug 2022 • Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Bing Xu, Changwen Zheng, Fuchun Sun
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
1 code implementation • 11 Jan 2022 • Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Fuchun Sun, Changwen Zheng
To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness.
no code implementations • 29 Sep 2021 • Jiawei Liu, Hang Gao, Yunfeng Hu, Xun Gong
The proxy dataset selection stage calculates the proposed average patch saliency (APS) of each available dataset to select a high-APS proxy dataset that can guarantee patches' fooling abilities.
no code implementations • 6 Sep 2021 • Jiangmeng Li, Wenwen Qiang, Hang Gao, Bing Su, Farid Razzak, Jie Hu, Changwen Zheng, Hui Xiong
To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.
no code implementations • EMNLP 2021 • Mengting Hu, Honglei Guo, Shiwan Zhao, Hang Gao, Zhong Su
A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way.
1 code implementation • NeurIPS 2021 • Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean
HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space.
no code implementations • ACL 2021 • Mengting Hu, Shiwan Zhao, Honglei Guo, Chao Xue, Hang Gao, Tiegang Gao, Renhong Cheng, Zhong Su
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
no code implementations • 1 Feb 2021 • Hang Gao, Mengting Hu, Renhong Cheng, Tiegang Gao
Answer selection is a task to choose the positive answers from a pool of candidate answers for a given question.
1 code implementation • ECCV 2020 • Zhe Cao, Hang Gao, Karttikeya Mangalam, Qi-Zhi Cai, Minh Vo, Jitendra Malik
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
no code implementations • 25 Mar 2020 • Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, Latifur Khan
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS).
no code implementations • 10 Oct 2019 • Karan K. Budhraja, Hang Gao, Tim Oates
A low time-complexity and data requirement favoring framework for reproducing emergent behavior, given an abstract demonstration, is discussed in [1], [2].
no code implementations • 10 Oct 2019 • Hang Gao, Tim Oates
Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability.
2 code implementations • ICLR 2020 • Hang Gao, Xizhou Zhu, Steve Lin, Jifeng Dai
This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field.
Ranked #183 on Object Detection on COCO test-dev
1 code implementation • 4 Dec 2018 • Roei Herzig, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, Trevor Darrell
Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance.
1 code implementation • ICCV 2019 • Hang Gao, Huazhe Xu, Qi-Zhi Cai, Ruth Wang, Fisher Yu, Trevor Darrell
A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated.
no code implementations • NeurIPS 2018 • Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data.
no code implementations • ECCV 2018 • Zheng Shou, Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Ranked #16 on Weakly Supervised Action Localization on ActivityNet-1.2 (mAP@0.5 metric)
Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1
1 code implementation • 22 Jul 2018 • Zheng Shou, Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization