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arXiv 2022年

作者： Wang, Chuwei Li, Shanda He, Di Wang, Liwei School of Mathematical Sciences Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Data Science Peking University China Zhejiang Lab China

The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The L2 Physics-Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relationship between the loss function and the approximation quality of the learned solution. In particular, we leverage the concept of stability in the literature of partial differential equation to study the asymptotic behavior of the learned solution as the loss approaches zero. With this concept, we study an important class of high-dimensional non-linear PDEs in optimal control, the Hamilton-Jacobi-Bellman (HJB) Equation, and prove that for general Lp Physics-Informed Loss, a wide class of HJB equation is stable only if p is sufficiently large. Therefore, the commonly used L2 loss is not suitable for training PINN on those equations, while L∞ loss is a better choice. Based on the theoretical insight, we develop a novel PINN training algorithm to minimize the L∞ loss for HJB equations which is in a similar spirit to adversarial training. The effectiveness of the proposed algorithm is empirically demonstrated through experiments. Our code is released at https://***/LithiumDA/L_inf-PINN. Copyright © 2022, The Authors. All rights reserved.

关键词： Partial differential equations

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Cross-Store Next-Basket Recommendation

Cross-Store Next-Basket Recommendation

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IEEE International Conference on data Mining (ICDM)

作者： Liang-Chen Ma Ya Li Zi-Feng Mai Fei-Yao Liang Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

关键词： Neural networks Collaboration data models Bipartite graph data mining

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META-learning INITIALIZATIONS FOR LOW-RESOURCE DRUG DISCOVERY

arXiv

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arXiv 2020年

作者： Nguyen, Cuong Q. Kreatsoulas, Constantine Branson, Kim M. GlaxoSmithKline Artificial Intelligence & Machine Learning GlaxoSmithKline Data & Computational Science

Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, drug discovery projects are often characterized by limited labeled data, hindering the applications of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the efficiency of the Model-Agnostic Meta-learning (MAML) algorithm – along with its variants FO-MAML and ANIL – at learning to predict chemical properties and activities. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 7.2% and 14.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with k ∈ {16, 32, 64, 128, 256} instances. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemical properties

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Contrastive learning for Adapting Language Model to Sequential Recommendation

Contrastive Learning for Adapting Language Model to Sequenti...

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IEEE International Conference on data Mining (ICDM)

作者： Fei-Yao Liang Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

关键词： Measurement Adaptation models Large language models Semantics Collaboration Contrastive learning Transformers data models data mining Recommender systems

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RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion

RecCoder: Reformulating Sequential Recommendation as Large L...

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IEEE International Conference on data Mining (ICDM)

作者： Kai-Huang Lai Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master.

关键词： Adaptation models Codes Accuracy Large language models Semantics Cognition data models data mining Recommender systems Testing

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QUASI-CONVERGENCE OF AN IMPLEMENTATION OF OPTIMAL BALANCE BY BACKWARD-FORWARD NUDGING

arXiv

引用

arXiv 2022年

作者： Masur, Gökce Tuba Mohamad, Haidar Oliver, Marcel Institut für Atmosphäre und Umwelt Goethe-Universität Frankfurt Frankfurt/Main60438 Germany Mathematical Institute for Machine Learning and Data Science KU Eichstätt–Ingolstadt Ingolstadt85049 Germany School of Engineering and Science Jacobs University Bremen28759 Germany

Optimal balance is a non-asymptotic numerical method for computing a point on an elliptic slow manifold for two-scale dynamical systems with strong gyroscopic forces. It works by solving a modified differential equation as a boundary value problem in time, where the nonlinear terms are adiabatically ramped up from zero to the fully nonlinear dynamics. A dedicated boundary value solver, however, is often not directly available. The most natural alternative is a nudging solver, where the problem is repeatedly solved forward and backward in time and the respective boundary conditions are restored whenever one of the temporal end points is visited. In this paper, we show quasi-convergence of this scheme in the sense that the termination residual of the nudging iteration is as small as the asymptotic error of the method itself, i.e., under appropriate assumptions exponentially small. This confirms that optimal balance in its nudging formulation is an effective algorithm. Further, it shows that the boundary value problem formulation of optimal balance is well posed up at most a residual error as small as the asymptotic error of the method itself. The key step in our proof is a careful two-component Gronwall *** Codes Primary 34E13, Secondary 34B15, 37M21 Copyright © 2022, The Authors. All rights reserved.

关键词： Boundary value problems

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Achieving Linear Speedup with Network-Independent learning Rates in Decentralized Stochastic Optimization

Achieving Linear Speedup with Network-Independent Learning R...

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IEEE Conference on Decision and Control

作者： Hao Yuan Sulaiman A. Alghunaim Kun Yuan Center for Machine Learning Research Peking University Beijing P. R. China Dept. Electrical Engr. Kuwait University Safat Kuwait AI for Science Institute Beijing P. R. China National Engineering Labratory for Big Data Analytics and Applications Beijing P. R. China

Decentralized stochastic optimization has become a crucial tool for addressing large-scale machine learning and control problems. In decentralized algorithms, all computing nodes are connected through a network topology, and each node communicates only with its direct neighbors. Decentralized algorithms can significantly reduce communication overhead by eliminating the need for global communication. However, existing research on the linear speedup analysis of decentralized stochastic algorithms is limited to the condition of network-dependent learning rates, which rarely holds in practice since the network connectivity is typically unknown to each node. As a result, it remains an open question whether a linear speedup bound can be achieved using network-independent learning rates. This paper provides an affirmative answer. By utilizing a new analysis framework, we prove that D-SGD and Exact-Diffusion, two representative decentralized stochastic algorithms, can achieve linear speedup with network-independent learning rates. Simulations are provided to validate our theories.

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Design and Implementation of a data Governance Framework and Platform: A Case Study of a National Research Organization of Thailand

Design and Implementation of a Data Governance Framework and...

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International Joint Conference on Computer science and Software Engineering (JCSSE)

作者： Sapa Chanyachatchawan Krich Nasingkun Patipat Tumsangthong Porntiwa Chata Marut Buranarach Monsak Socharoentum Leveraging Technology Solutions Section National Electronics and Computer Technology Center Bangkok Thailand Strategic Analytics Networks with Machine Learning and AI Research National Electronics and Computer Technology Center Bangkok Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Bangkok Thailand Digital Government Development Agency Bangkok Thailand

In the current era of extensive data usage across industries, data collection, preservation, utilization, and organization has become more challenging and nuanced because it is necessary to consider critical concerns such as data security, privacy, and legal issues, apart from efficiency issues. As a result, Thai government initiated the idea and effort to implement data governance throughout the government agency. This paper showcases the implementation of data governance in a governmental research organization with highly diverse structured and unstructured data. The implementation follows international standards and the guidelines of the Digital Government Development Agency (DGA). The executives set up the working body, including the data Governance Council and data Stewards, responsible for setting up and deploying policies and regulations. Creating awareness and the necessary infrastructure are the main focuses in the first-year phase. The metadata was designed to extend DGA's version and match the organization's unique requirements. A data catalog platform was developed accordingly. We organized activities to boost employee awareness and participation, including advertising and data catalog platform training. By the end of the first year of implementation, every organization unit had registered at least one data record into the data catalog.

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Topic Modelling using Transfer learning: Issues and Challenges

Topic Modelling using Transfer Learning: Issues and Challeng...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Preethi Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

A machine learning method, transfer learning, uses information from one job or area to enhance effectiveness in a related but distinct task or area. Transfer learning enables using pre-trained models that were previously learned using a sizable and varied dataset in the original domain instead of creating a model from the beginning for the intended activity. Usually, using a lesser dataset, the pre-trained model's representations and knowledge may be improved upon or applied to the intended goal or area. In natural language processing (NLP) and machine learning, topic modeling is an approach that finds and extracts the fundamental concepts or subjects from a group of documents or textual data. Despite prior experience with the aspects, this unsupervised learning method seeks to uncover the text's latent semantic structure. This paper's primary goal is to investigate the possibilities of transfer learning for text topic modeling. Additionally, to assess the correctness of the pre-trained model's performance. The paper also describes the issues and challenges associated with the text for performing the topic modeling using transfer learning. This paper delves into the methodologies and techniques of topic modeling using transfer learning. It explores how this novel approach transcends the constraints of conventional topic modeling, enabling the extraction of meaningful topics from data-rich source domains to improve topic modeling performance in data-scarce or specialized target domains. By integrating the principles of transfer learning into the topic modeling framework, we unlock the potential to enhance the interpretability, accuracy, and applicability of topic modeling across diverse domains and applications. This paper sheds light on the transformative power of transfer learning in topic modeling.

关键词： Performance evaluation Adaptation models Accuracy Computational modeling Transfer learning Semantics data models Natural language processing data mining Unsupervised learning

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Parkinson’s Disease Detection using Hand Drawn Spirals and Waves

Parkinson’s Disease Detection using Hand Drawn Spirals and ...

引用

Recent Advances in science and Engineering Technology (ICRASET), International Conference on

作者： Shashidhara H S Saish K Kalbag Tanya Mathias Srinivasa K G Niranjanamurthy M Department of Informaition Science and Engineering M S Ramaiah Institute of Techology and Engineering Affiliated to Visvesvaraya Technological University Belgaum Bangalore India Department of Data Science and Artififial Intelligece International Institute of Information Technology Naya Raipur Chhattisgarh India Department of Artificial Intelligence and Machine Learning B M S Institute of Technology and Management Affiliated to Visvesvaraya Technological University Belgaum Bangalore India

Parkinson’s disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson’s disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson’s disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson’s disease by analyzing hand-drawn spirals and waves in photographs.

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