no code implementations • 31 Oct 2024 • Zhuoyang Pan, Angjoo Kanazawa, Hang Gao
SOAR leverages structural normal prior and generative diffusion prior to address such an ill-posed reconstruction problem.
no code implementations • 6 Oct 2024 • Ehsan Ebrahimzadeh, Nikhil Monga, Hang Gao, Alex Cozzi, Abraham Bagherjeiran
We build empirical estimates for the expected reward of the marketplace from observational data that account for the heterogeneity of economic value across session contexts as well as the distribution shifts in learning from observational user activity data.
no code implementations • 25 Sep 2024 • Hang Gao, Shuohua Yang, Xinli Liu
Weather parametric insurance relies on weather indices rather than actual loss assessments, enhancing claims efficiency, reducing moral hazard, and improving fairness.
1 code implementation • 13 Sep 2024 • Chengyu Yao, Hong Huang, Hang Gao, Fengge Wu, Haiming Chen, Junsuo Zhao
In particular, we propose a specially designed graph that leverages graph kernel algorithms to represent the similarity between molecules quantitatively.
no code implementations • 8 Sep 2024 • Hang Gao, Xinming Wu, Luming Liang, Hanlin Sheng, Xu Si, Gao Hui, Yaxing Li
This model integrates a pre-trained vision foundation model (VFM) with a sophisticated multi-modal prompt engine.
1 code implementation • 13 Aug 2024 • Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng
Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs.
no code implementations • 18 Jul 2024 • Qianqian Wang, Vickie Ye, Hang Gao, Jake Austin, Zhengqi Li, Angjoo Kanazawa
Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task.
no code implementations • 5 Jul 2024 • Hang Gao, Yongfeng Zhang
This establishes the profound difficulty of pursuing similarity and diversity simultaneously in vector retrieval and lays a theoretical groundwork for further research.
1 code implementation • 18 Jun 2024 • Xunzhi Wang, Zhuowei Zhang, Qiongyu Li, Gaonan Chen, Mengting Hu, Zhiyu Li, Bitong Luo, Hang Gao, Zhixin Han, Haotian Wang
The rapid development of large language models (LLMs) has shown promising practical results.
no code implementations • 13 Jun 2024 • Hang Gao, Peng Qiao, Yifan Jin, Fengge Wu, Jiangmeng Li, Changwen Zheng
However, within the domain of graph representation learning, the inherent complexity of graph data obstructs the derivation of a comprehensive causal structure that encapsulates universal rules or relationships governing the entire dataset.
1 code implementation • 11 Jun 2024 • Yinhao Bai, Yalan Xie, Xiaoyi Liu, Yuhua Zhao, Zhixin Han, Mengting Hu, Hang Gao, Renhong Cheng
To tackle this issue, we further propose a Broadview Soft Prompting (BvSP) method that aggregates multiple templates with a broader view by taking into account the correlation between the different templates.
1 code implementation • 15 Apr 2024 • Hang Gao, Yongfeng Zhang
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, but are constrained by the comprehensiveness and diversity of the provided examples, leading to outputs that often diverge significantly from expected results, especially when it comes to the open-ended questions.
no code implementations • 18 Mar 2024 • Hang Gao, Jiaguo Yuan, Jiangmeng Li, Peng Qiao, Fengge Wu, Changwen Zheng, Huaping Liu
To address this issue, we have introduced Partial Label Learning (PLL) into graph representation learning.
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.
Multi-agent Reinforcement Learning reinforcement-learning +2
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 • 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.
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.
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 #184 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