Search Results for author: Hang Dong

Found 39 papers, 25 papers with code

A Language Model based Framework for New Concept Placement in Ontologies

no code implementations27 Feb 2024 Hang Dong, Jiaoyan Chen, Yuan He, Yongsheng Gao, Ian Horrocks

In all steps, we propose to leverage neural methods, where we apply embedding-based methods and contrastive learning with Pre-trained Language Models (PLMs) such as BERT for edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder, and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for edge selection.

Contrastive Learning Entity Linking +1

COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy

no code implementations13 Jan 2024 Lu Wang, Mayukh Das, Fangkai Yang, Chao Duo, Bo Qiao, Hang Dong, Si Qin, Chetan Bansal, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk.

Imitation Learning Management

TaskWeaver: A Code-First Agent Framework

1 code implementation29 Nov 2023 Bo Qiao, Liqun Li, Xu Zhang, Shilin He, Yu Kang, Chaoyun Zhang, Fangkai Yang, Hang Dong, Jue Zhang, Lu Wang, Minghua Ma, Pu Zhao, Si Qin, Xiaoting Qin, Chao Du, Yong Xu, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang

TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.

Natural Language Understanding

Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System

1 code implementation9 Nov 2023 Xiangguo Sun, Hong Cheng, Hang Dong, Bo Qiao, Si Qin, QIngwei Lin

To establish such scoring systems, several "empirical criteria" are firstly determined, followed by dedicated top-down design for each factor of the score, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario.

Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

no code implementations29 Sep 2023 Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, Valentina Tamma

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.

Knowledge Graphs Management

Exploring Large Language Models for Ontology Alignment

1 code implementation12 Sep 2023 Yuan He, Jiaoyan Chen, Hang Dong, Ian Horrocks

This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies.

Large Language Models Vote: Prompting for Rare Disease Identification

2 code implementations24 Aug 2023 David Oniani, Jordan Hilsman, Hang Dong, Fengyi Gao, Shiven Verma, Yanshan Wang

This method achieves improved results to any one model in the ensemble on one-shot rare disease identification and classification tasks.

Few-Shot Learning

Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution

1 code implementation5 Aug 2023 Yong liu, Hang Dong, Boyang Liang, Songwei Liu, Qingji Dong, Kai Chen, Fangmin Chen, Lean Fu, Fei Wang

Since the high resolution of intermediate features in SISR models increases memory and computational requirements, efficient SISR transformers are more favored.

Image Super-Resolution

Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction

1 code implementation1 Aug 2023 Zhangchi Zhu, Lu Wang, Pu Zhao, Chao Du, Wei zhang, Hang Dong, Bo Qiao, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang

To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.

DeepOnto: A Python Package for Ontology Engineering with Deep Learning

1 code implementation6 Jul 2023 Yuan He, Jiaoyan Chen, Hang Dong, Ian Horrocks, Carlo Allocca, Taehun Kim, Brahmananda Sapkota

Applying deep learning techniques, particularly language models (LMs), in ontology engineering has raised widespread attention.

Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement

1 code implementation26 Jun 2023 Hang Dong, Jiaoyan Chen, Yuan He, Ian Horrocks

Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e. g., ontologies and taxonomies.

Language Modelling Large Language Model

Introspective Tips: Large Language Model for In-Context Decision Making

no code implementations19 May 2023 Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang, Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.

Decision Making Language Modelling +2

Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking

3 code implementations14 Feb 2023 Hang Dong, Jiaoyan Chen, Yuan He, Yinan Liu, Ian Horrocks

We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity.

Entity Linking

Language Model Analysis for Ontology Subsumption Inference

1 code implementation14 Feb 2023 Yuan He, Jiaoyan Chen, Ernesto Jiménez-Ruiz, Hang Dong, Ian Horrocks

Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently.

Language Modelling Natural Language Inference +1

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

no code implementations21 Nov 2022 Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.

Multi-agent Reinforcement Learning reinforcement-learning +1

Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization

1 code implementation18 Jul 2022 Yuhao Huang, Hang Dong, Jinshan Pan, Chao Zhu, Yu Guo, Ding Liu, Lean Fu, Fei Wang

We develop two simple yet effective plug and play methods to improve the performance of existing local and non-local propagation-based VSR algorithms on widely-used public videos.

Video Super-Resolution

Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

2 code implementations6 May 2022 Yuan He, Jiaoyan Chen, Hang Dong, Ernesto Jiménez-Ruiz, Ali Hadian, Ian Horrocks

Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques.

Ontology Matching

Experimental quantum adversarial learning with programmable superconducting qubits

no code implementations4 Apr 2022 Wenhui Ren, Weikang Li, Shibo Xu, Ke Wang, Wenjie Jiang, Feitong Jin, Xuhao Zhu, Jiachen Chen, Zixuan Song, Pengfei Zhang, Hang Dong, Xu Zhang, Jinfeng Deng, Yu Gao, Chuanyu Zhang, Yaozu Wu, Bing Zhang, Qiujiang Guo, Hekang Li, Zhen Wang, Jacob Biamonte, Chao Song, Dong-Ling Deng, H. Wang

Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.

BIG-bench Machine Learning Quantum Machine Learning

Automated Clinical Coding: What, Why, and Where We Are?

1 code implementation21 Mar 2022 Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu

Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.

Contextual Semantic Embeddings for Ontology Subsumption Prediction

2 code implementations20 Feb 2022 Jiaoyan Chen, Yuan He, Yuxia Geng, Ernesto Jimenez-Ruiz, Hang Dong, Ian Horrocks

Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.

Knowledge Graph Embeddings Language Modelling +1

A Unified Review of Deep Learning for Automated Medical Coding

no code implementations8 Jan 2022 Shaoxiong Ji, Wei Sun, Xiaobo Li, Hang Dong, Ara Taalas, Yijia Zhang, Honghan Wu, Esa Pitkänen, Pekka Marttinen

Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.

Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring

1 code implementation9 Dec 2021 Chao Zhu, Hang Dong, Jinshan Pan, Boyang Liang, Yuhao Huang, Lean Fu, Fei Wang

Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring.

Deblurring Video Restoration

Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property

no code implementations ICLR 2022 Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, QIngwei Lin

Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making.

Combinatorial Optimization Decision Making

Learning To Restore Hazy Video: A New Real-World Dataset and a New Method

no code implementations CVPR 2021 Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Fei Wang

On the other hand, the video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets.

Image Dehazing

Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision

1 code implementation5 May 2021 Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang, Minhong Wang, Emma Whitfield, Honghan Wu

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts.

Entity Linking

Mixed-Initiative Level Design with RL Brush

1 code implementation6 Aug 2020 Omar Delarosa, Hang Dong, Mindy Ruan, Ahmed Khalifa, Julian Togelius

This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation.

reinforcement-learning Reinforcement Learning (RL)

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

1 code implementation CVPR 2020 Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, Ming-Hsuan Yang

To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme.

Image Dehazing

Gated Fusion Network for Degraded Image Super Resolution

1 code implementation2 Mar 2020 Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang

To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.

Image Super-Resolution

Joint Multi-Label Attention Networks for Social Text Annotation

no code implementations NAACL 2019 Hang Dong, Wei Wang, Kai-Zhu Huang, Frans Coenen

To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content.

Sentence text annotation

Gated Fusion Network for Joint Image Deblurring and Super-Resolution

2 code implementations27 Jul 2018 Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.

Computational Efficiency Deblurring +2

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