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Towards Task-Conflicts Momentum-Calibrated Approach for Multi-task learning 40

Towards Task-Conflicts Momentum-Calibrated Approach for Mult...

引用

40th IEEE international conference on data Engineering, ICDE 2024

作者： Chai, Heyan Liu, Zeyu Tong, Yongxin Yao, Ziyi Fang, Binxing Liao, Qing School of Computer Science and Engineering Beihang University China China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350317152

Multi-task learning (MTL) has succeeded in various industrial applications by utilizing common knowledge among joint training tasks to enhance the generalization of MTL models, resulting in improved performance across all training tasks simultaneously. Unfortunately, training all tasks simultaneously often causes performance degradation compared to single-task models since different tasks might conflict with each other. Despite existing MTL methods that aim to mitigate task conflicts by manipulating task gradients at each iteration, they ignore the potential influence of noisy data from different batches on task gradients. Consequently, the current iteration's task gradient may not accurately reflect the task itself, leading to inadequate alleviation of the dilemma of task conflicts. Moreover, existing works seldom explore the potential source of task conflicts and merely pose an assumption. In this paper, we conduct an in-depth empirical investigation into the potential sources of performance degradation of MTL and find that task gradient conflict is one of the primary reasons for the performance degradation of tasks. Then, to address the task conflicts problem, we propose a novel gradient manipulation approach, namely MoCoGrad, which manipulates task gradients by leveraging the momentum information of the task to calibrate the gradients of conflicting tasks. In addition, we derive theoretical guarantees for the con-vergence of our proposed MoCoGrad and theoretically analyze the convergence rate of MoCoGrad. Finally, to evaluate the effectiveness of MoCoGrad, extensive experiments are conducted on six real-world datasets from different domains. Our approach yields the best performance across all tasks in all six MTL benchmarks, demonstrating the effectiveness and superiority of our method. © 2024 IEEE.

关键词： Iterative methods

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Formal XAI via Syntax-Guided Synthesis 1st

Formal XAI via Syntax-Guided Synthesis

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1st international conference on Bridging the Gap between AI and Reality, AISoLA 2023

作者： Bjørner, Katrine Judson, Samuel Cano, Filip Goldman, Drew Shoemaker, Nick Piskac, Ruzica Könighofer, Bettina New York University New York United States Yale University New Haven United States Graz University of Technology Graz Austria University of Virginia Charlottesville United States

ISBN: (纸本)9783031460012

In this paper, we propose a novel application of syntax-guided synthesis to find symbolic representations of a model’s decision-making process, designed for easy comprehension and validation by humans. Our approach takes input-output samples from complex machine learning models, such as deep neural networks, and automatically derives interpretable mimic programs. A mimic program precisely imitates the behavior of an opaque model over the provided data. We discuss various types of grammars that are well-suited for computing mimic programs for tabular and image input data. Our experiments demonstrate the potential of the proposed method: we successfully synthesized mimic programs for neural networks trained on the MNIST and the Pima Indians diabetes data sets. All experiments were performed using the SMT-based cvc5 synthesis tool. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Explainable machine learning Program Synthesis Programming by Example (PbE) Syntax-Guided Synthesis (SyGuS)

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Ocean data Quality Assessment through Outlier Detection-enhanced Active learning

Ocean Data Quality Assessment through Outlier Detection-enha...

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2023 IEEE international conference on Big data, Bigdata 2023

作者： Li, Na Qi, Yiyang Xin, Ruyue Zhao, Zhiming Vrije Universiteit Amsterdam Computer Science Department Amsterdam Netherlands Informatics Institute University of Amsterdam Multiscale Networked System Netherlands Netherlands Amsterdam Netherlands

ISBN: (纸本)9798350324457

Ocean and climate research benefits from global ocean observation initiatives such as Argo, GLOSS, and EMSO. The Argo network, dedicated to ocean profiling, generates a vast volume of observatory data. However, data quality issues from sensor malfunctions and transmission errors necessitate stringent quality assessment. Existing methods, including machine learning, fall short due to limited labeled data and imbalanced datasets. To address these challenges, we propose an Outlier Detection-Enhanced Active learning (ODEAL) framework for ocean data quality assessment, employing Active learning (AL) to reduce human experts' workload in the quality assessment workflow and leveraging outlier detection algorithms for effective model initialization. We also conduct extensive experiments on five large-scale realistic Argo datasets to gain insights into our proposed method, including the effectiveness of AL query strategies and the initial set construction approach. The results suggest that our framework enhances quality assessment efficiency by up to 465.5% with the uncertainty-based query strategy compared to random sampling and minimizes overall annotation costs by up to 76.9% using the initial set built with outlier detectors. © 2023 IEEE.

关键词： active learning Argo initial set construction machine learning ocean data quality control

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Optimizing ML-Based Solar PV Forecasting Models in Smart Grids

Optimizing ML-Based Solar PV Forecasting Models in Smart Gri...

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2024 IEEE Global Energy conference, GEC 2024

作者： Ozdemir, Gokcen Kuzlu, Murat Ozdemir, Umut Catak, Ferhat Ozgur Electrical Engineering Technology Old Dominion University NorfolkVA United States Halil Bayraktar Vocational School of Health Services Erciyes University Kayseri Turkey Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway

ISBN: (纸本)9798331532611

In this paper, the impact of data pre-processing strategies on the performance of forecasting models for solar power generation is comprehensively analyzed. The integration of outlier detection, normalization, and missing data handling techniques with popular ML models such as XGBoost, LightGBM, and Random Forest is investigated. The effects of six different pre-processing methods on three different ML models are examined over a total of 81 possible combinations. Two different scenarios have been used: one with a dataset without missing values (Scenario 1) and one with a dataset with missing values (Scenario 2). The results highlight that data pre-processing can significantly affect the accuracy and the importance of specific pre-processing methods in optimizing ML models for specific datasets and algorithms. ©2024 IEEE.

关键词： machine learning Solar PV power generation forecasting

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Active-MTSAD: Multivariate Time Series Anomaly Detection With Active learning 52

Active-MTSAD: Multivariate Time Series Anomaly Detection Wit...

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52nd Annual IEEE/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Wang, Wenlu Chen, Pengfei Xu, Yibin He, Zilong Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665416931

ISBN: (纸本)9781665416931

Time series anomaly detection is an important research topic in the field of intelligent operation and maintenance. When software systems are frequently updated with continuous integration and deployment, the distribution of KPI data will also change, and the accuracy of anomaly detection models will inevitably decrease. To tackle this problem, we propose an active anomaly detection framework named Active-MTSAD suitable for multi-dimensional time series, combining unsupervised anomaly detection and active learning. The active learning module introduces three feedback strategies, namely denominator penalty, negative penalty, and metric learning, to learn new anomalous patterns under new data distribution. In metric learning, we consider the difference between normal and abnormal samples in reconstruction error and latent space. We conduct extensive experiments on a large-scale public dataset and a real-world dataset coming from Tencent. The experimental results show that Active-MTSAD can still achieve excellent performance in real scenarios where the distribution changes with only 0.2(% of labels.

关键词： Active learning Metric learning Feedback Anomaly detection

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Methods of machine learning in Epidemic Risk Assessment: The Case of the Covid-19 4

Methods of Machine Learning in Epidemic Risk Assessment: The...

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4th international conference for Emerging Technology, INCET 2023

作者： Deshmukh, Sanket Kaur, Savleen Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350335750

SARS-CoV-2, also referred to as the acute respiratory syndrome with coronavirus, first surfaced in late 2019. It was later determined to be a fatal illness that is wreaking havoc on the global economy. It was designated a pandemic by the World Health Organization (WHO) in March 2020. Numerous scientific fields have seen the introduction of research to address COVID-19-related issues. In our daily lives, a number of issues are solved using machine learning(ML), is one of the most effective recent tales. The management and containment of pandemics were also looked at. The standard test for diagnosing COVID-19 is the Polymerase Chain Reaction (PCR) but CT scans, X-rays, blood tests, and sound investigations can also be used. This study helps researchers to understand and analyze data patterns connected with COVID-19. By doing so, the knowledge gap between researchers and the available data is reduced. Get ready for a potential COVID-19 epidemic that might happen in the future. Future obstacles and ways forward are also mentioned. © 2023 IEEE.

关键词： Coronavirus

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Automatic Thai Ticket Classification By Using machine learning For IT Infrastructure Company 19

Automatic Thai Ticket Classification By Using Machine Learni...

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19th international Joint conference on Computer science and Software Engineering (JCSSE)

作者： Khowongprasoed, Kraidet Titijaroonroj, Taravichet King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Bangkok 10520 Thailand

ISBN: (数字)9781665485104

ISBN: (纸本)9781665485104

Ticket classification is a process to define the category name of each ticket before assigning the resolution team to serve each ticket. It is an important process to support the customers inside and outside the company. It can make customer dissatisfaction if the processing time is high or delayed. Based on the recording data in 2019 - 2021 at the studying company, we found that the manual ticket classification got an error rate about 53 percent because the office workers misunderstand. To alleviate this problem, we propose the methodology for automatic Thai ticket classification by using Term Frequency-Inverse Document Frequency with Support Vector machine. The experimental result shows that the performance of the proposed methodology is higher than the manual classification by 2 times or 41 percent.

关键词： Thai Ticket Classification IT Service and Incident Ticket Term Frequency-Inverse Document Frequency Support Vector machine

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Physics-Based machine learning Hydraulic Piston Pump Performance Degradation and Prognostics 7

Physics-Based Machine Learning Hydraulic Piston Pump Perform...

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7th international conference on System Reliability and Safety, ICSRS 2023

作者： Zhang, Yadong Wang, Shaoping Zhang, Chao Chen, Rentong School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350306057

The hydraulic piston pump is an important component in the hydraulic system, and it has always been an important problem to accurately and effectively estimate the degradation state of the pump. Physical model-based and data-driven fusion methods can overcome the shortcomings of the two models, and have achieved success in the fields of prognosis and health management. This paper proposes a physics-based deep learning framework for degradation assessment and remaining useful life (RUL) prediction of hydraulic piston pumps. Firstly, the failure physics of the three friction pairs of the hydraulic piston pump is analyzed, and the mathematical calculation formula of the wear amount is obtained. Then, the calculated wear of the three friction pairs is used as the input with physical information, and is fused with the long short term memory (LSTM) network. Finally, the effectiveness of the proposed method is verified by the data of the hydraulic piston pump. It shows that the proposed framework has good capability of degradation assessment and RUL prediction. © 2023 IEEE.

关键词： Long short-term memory

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Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques 23

Optimizing Tensor Computations: From Applications to Compila...

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2023 ACM/SIGMOD international conference on Management of data, SIGMOD 2023

作者： Boehm, Matthias Interlandi, Matteo Jermaine, Chris TU Berlin Berlin Germany Mircosoft GSL Los AngelesCA United States Rice University HoustonTX United States

ISBN: (纸本)9781450395076

machine learning (ML) training and scoring fundamentally relies on linear algebra programs and more general tensor computations. Most ML systems utilize distributed parameter servers and similar distribution strategies for mini-batch stochastic gradient descent training. However, many more tasks in the data science and engineering lifecycle can benefit from efficient tensor computations. Examples include primitives for data cleaning, data and model debugging, data augmentation, query processing, numerical simulations, as well as a wide variety of training and scoring algorithms. In this survey tutorial, we first make a case for the importance of optimizing more general tensor computations, and then provide an in-depth survey of existing applications, optimizing compilation techniques, and underlying runtime strategies. Interestingly, there are close connections to data-intensive applications, query rewriting and optimization, as well as query processing and physical design. Our goal for the tutorial is to structure existing work, create common terminology, and identify open research challenges. © 2023 ACM.

关键词： machine learning

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Contrastive learning with Boosted Memorization 39

Contrastive Learning with Boosted Memorization

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39th international conference on machine learning (ICML)

作者： Zhou, Zhihan Yao, Jiangchao Wang, Yanfeng Han, Bo Zhang, Ya Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

Self-supervised learning has achieved a great success in the representation learning of visual and textual data. However, the current methods are mainly validated on the well-curated datasets, which do not exhibit the real-world long-tailed distribution. Recent attempts to consider self-supervised long-tailed learning are made by rebalancing in the loss perspective or the model perspective, resembling the paradigms in the supervised long-tailed learning. Nevertheless, without the aid of labels, these explorations have not shown the expected significant promise due to the limitation in tail sample discovery or the heuristic structure design. Different from previous works, we explore this direction from an alternative perspective, i.e., the data perspective, and propose a novel Boosted Contrastive learning (BCL) method. Specifically, BCL leverages the memorization effect of deep neural networks to automatically drive the information discrepancy of the sample views in contrastive learning, which is more efficient to enhance the long-tailed learning in the label-unaware context. Extensive experiments on a range of benchmark datasets demonstrate the effectiveness of BCL over several state-of-the-art methods. Our code is available at https://***/MediaBrain-SJTU/BCL.

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