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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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An Efficient Graphics data Retrieval Method Based on data Conversion Schemes

An Efficient Graphics Data Retrieval Method Based on Data Co...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Baek, Nakhoon School of Computer Science and Engineering Kyungpook National University Daegu41566 Korea Republic of Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Data-Driven Intelligent Mobility ICT Research Center Kyungpook National University Daegu41566 Korea Republic of

ISBN: (纸本)9798331510756

Modern computer graphics systems usually manage a set of internal data to store control parameters and all kind of graphics data. The behind problem on these graphics system data is that they are frequently updated and retrieved with variety of data types, even they are initially stored as specific data types. To support these updates and retrievals with various data types, modern graphics systems typically use data type conversion features, and even in the worst cases, they should provide all combinations of internally stored data types and newly provided or retrieved data types. In this paper, we show how to efficiently provide all the required combinations of data type conversions, with motivations, analysis, and overall designs. We also show typical type conversion scenarios, with specific API function examples. Our design shows that it can improve the internal architecture and the internal efficiency of modern computer graphics systems. In its implementation, we use C++std:: map container for fast lookup of the best-suitable conversion function. This overall scheme was integrated in our existing systems, and shows its feasibility and correctness. © 2025 IEEE.

关键词： data assimilation

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning 2

Revolutionizing Machine Learning Security: The Role of Quant...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Planning with Logical Graph-Based Language Model for Instruction Generation 27

Planning with Logical Graph-Based Language Model for Instruc...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhang, Fan Jin, Kebing Zhuo, Hankz Hankui School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9781643685489

Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture strict logic from free-form texts. In this paper, we propose a novel graph-based language model, Logical-GLM, to extract strict logic from free-form texts and then infuse into language models. Specifically, we first capture information from natural language instructions and construct logical probability graphs that generally describe domains. Next, we generate logical skeletons to guide language model training, infusing domain knowledge into language models. At last, we alternately optimize the searching policy of graphs and language models until convergence. The experimental results show that Logical-GLM is both effective and efficient compared with traditional language models, despite using smaller-scale training data and fewer parameters. Our approach can generate instructional texts with more correct logic owing to the internalized domain knowledge. Moreover, the search of logical graphs reflects the inner mechanism of the language models, which improves the interpretability of black-box models. © 2024 The Authors.

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Learning dynamics of kernel-based deep neural networks in manifolds

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science China(Information sciences) 2021年第11期64卷 105-119页

作者： Wei WU Xiaoyuan JING Wencai DU Guoliang CHEN School of Computer Science Wuhan University Institute of Deep-sea Science and Engineering Chinese Academy of Sciences School of Computer Guangdong University of Petrochemical Technology Institute of Data Science City University of Macau College of Computer Science and Software Engineering Shenzhen University

Convolutional neural networks(CNNs) obtain promising results via layered kernel convolution and pooling operations, yet the learning dynamics of the kernel remain obscure. We propose a continuous form to describe kernel-based convolutions through integration in neural manifolds. The status of spatial expression is proposed to analyze the stability of kernel-based CNNs. We divide CNN dynamics into the three stages of unstable vibration, collaborative adjusting, and stabilized fluctuation. According to the system control matrix of the kernel, the kernel-based CNN training proceeds via the unstable and stable status and is verified by numerical experiments.

关键词： learning dynamics kernel-based convolution manifolds control model network stability

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Object-Centric World Model for Language-Guided Manipulation

arXiv

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arXiv 2025年

作者： Jeong, Youngjoon Chun, Junha Cha, Soonwoo Kim, Taesup Graduate School of Data Science Department of Electrical and Computer Engineering

A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained significant attention due to the impressive success of diffusion models. However, these models require substantial computational resources. To address these challenges, we propose a world model leveraging object-centric representation space using slot attention, guided by language instructions. Our model perceives the current state as an object-centric representation and predicts future states in this representation space conditioned on natural language instructions. This approach results in a more compact and computationally efficient model compared to diffusion-based generative alternatives. Furthermore, it flexibly predicts future states based on language instructions, and offers a significant advantage in manipulation tasks where object recognition is crucial. In this paper, we demonstrate that our latent predictive world model surpasses generative world models in visuo-linguo-motor control tasks, achieving superior sample and computation efficiency. We also investigate the generalization performance of the proposed method and explore various strategies for predicting actions using object-centric representations. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

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Research on Frame and Table Recognition Based on CAD drawings

Research on Frame and Table Recognition Based on CAD drawing...

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2024 China Automation Congress, CAC 2024

作者： Zhong, Hui Miao, Yuanman Lìaonìng Province Big Data Management and Analysisline Laboratory of Urban Construction School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350368604

In the process of drawing architectural drawings with AutoCAD software, enterprises will produce a large number of CAD drawings in DWG format. The tables of these CAD drawings contain rich textual information. These text information constitute the key data base of enterprise systematic management drawings. At present, the systematic management of drawing data needs to collect these sheet data, but the sheet data collection mainly depends on manual input, which leads to high error rate of data collection, huge workload, long compilation cycle and low work efficiency. The purpose of this paper is to discuss how to deal with these tables efficiently. Firstly, based on Revit secondary development, the frame recognition algorithm is proposed, which confirms the inner and outer frame by finding the largest rectangle and the second largest rectangle. In addition, the Teigha class library is used to extract text, so as to extract key data, so as to promote the collection, reuse and systematic management of product data. Through the processing of drawing frames and forms, it lays a good foundation for subsequent component identification. The method proposed in this paper has strong versatility and adaptability, and can basically realize the recognition of CAD drawing frame and form, and extract form text, and achieve higher accuracy and efficiency. © 2024 IEEE.

关键词： Efficiency

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Road Detection Based on Fusion of LiDAR data and Visual data 1

Road Detection Based on Fusion of LiDAR Data and Visual Data

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1st International Conference on Intelligent Transportation and Smart Cities, ICITSC 2024

作者： Yang, Chaoju Rong, Jian Chin, Soon Ku School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

ISBN: (纸本)9781643685205

Smart transportation is a new development model for intelligent traffic based on the combination of the Internet, the Internet of Things, and other networks. Road detection is one of the foundational and core technologies in smart transportation. It provides accurate, reliable, and real-time road information for applications such as autonomous driving, intelligent traffic management, and urban planning. This contributes to the improvement of traffic safety, efficiency, and sustainability. However, relying solely on visual images for road detection still faces numerous challenges, such as changes in lighting conditions, image blurring, and occlusions. To enhance the robustness and accuracy of road detection, leveraging LiDAR (Light Detection and Ranging) data as supplementary information is considered. LiDAR data is not affected by visual noise and can offer more precise depth and height information. Nevertheless, effectively integrating and adapting to different data and feature spaces remain crucial challenges. This paper proposes an improved road detection method based on the PLARD (Progressive LiDAR Adaptation for Road Detection) network. It introduces the U-Net network as an additional branch, collaborating with the image and point cloud branches for feature fusion and road detection. The U-Net network utilizes information from the images for initial segmentation, providing a better prior, and enhances the feature fusion capability of the PLARD network, thereby improving segmentation accuracy and robustness. Experiments conducted on the KITTI Road dataset demonstrate that the proposed method outperforms the PLARD network and other baseline methods in terms of the Dice coefficient. Particularly, it excels in complex urban scenarios and exhibits strong generalization capabilities. The introduced road detection approach, combining LiDAR and visual data, provides an efficient, robust, and versatile solution for smart transportation. It adapts to different scenarios

关键词： Autonomous vehicles

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Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding 30

Hierarchical Topic Modeling via Contrastive Learning and Hyp...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lin, Zhicheng Chen, Hegang Lu, Yuyin Rao, Yanghui Xu, Hao Lai, Hanjiang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9782493814104

Hierarchical topic modeling, which can mine implicit semantics in the corpus and automatically construct topic hierarchical relationships, has received considerable attention recently. However, the current hierarchical topic models are mainly based on Euclidean space, which cannot well retain the implicit hierarchical semantic information in the corpus, leading to irrational structure of the generated topics. On the other hand, the existing Generative Adversarial Network (GAN) based neural topic models perform satisfactorily, but they remain constrained by pattern collapse due to the discontinuity of latent space. To solve the above problems, with the hypothesis of hyperbolic space, we propose a novel GAN-based hierarchical topic model to mine high-quality topics by introducing contrastive learning to capture information from documents. Furthermore, the distinct tree-like property of hyperbolic space preserves the implicit hierarchical semantics of documents in topic embeddings, which are projected into the hyperbolic space. Finally, we use a multi-head self-attention mechanism to learn implicit hierarchical semantics of topics and mine topic structure information. Experiments on real-world corpora demonstrate the remarkable performance of our model on topic coherence and topic diversity, as well as the rationality of the topic hierarchy. Our code is available at https://***/Adrian-LZC/hHTM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Embeddings

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction 30

A Semantic Mention Graph Augmented Model for Document-Level ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an China National University of Singapore Singapore

ISBN: (纸本)9782493814104

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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