Integrating Machine Learning with Human Knowledge

Deng, Changyu; Ji, Xunbi A.; Rainey, Colton; Zhang, Jianyu; Lü, Wei

doi:10.1016/j.isci.2020.101656

Cited by 129 publications

(75 citation statements)

References 195 publications

(206 reference statements)

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“…Machine learning (ML), coupled with big data, has been flourishing in recent years. Integrating human knowledge into machine learning (Deng et al, 2020) has achieved functions and performance not available before and facilitated the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. Beyond the field of computer and data sciences such as computer vision, natural language processing, image recognition, and search engine, machine learning is increasingly used in the field of physics (Carleo et al, 2019;Dunjko and Briegel, 2018), chemistry (Goh et al, 2017;Panteleev et al, 2018), biology (Silva et al, 2019;Zitnik et al, 2019), engineering (Flah et al, 2020;Kim et al, 2018;McCoy and Auret, 2019), and materials science (Morgan and Jacobs, 2020).…”

Section: Introduction and Overviewsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

Self Cite

106

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Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

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Section: Introduction and Overviewsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

Self Cite

106

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“…This phenomenon may explain why language models fine-tuned on pretrained checkpoints (Raffel et al, 2020;Qi et al, 2020; are achieving stateof-the-art results in abstractive summarization, as they are able to make use of outside information gained from the high volume of texts they were pretrained with. Additionally, it would be interesting to investigate whether the recent trend of incorporating real-world knowledge and commonsense reasoning (Tandon et al, 2018;Deng et al, 2020) into language models could improve text summarization performance.…”

Section: Human Evaluation Of Xl-summentioning

confidence: 99%

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

Hasan¹,

Bhattacharjee²,

Islam³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Contemporary works on abstractive text summarization have focused primarily on highresource languages like English, mostly due to the limited availability of datasets for low/midresource ones. In this work, we present XL-Sum, a comprehensive and diverse dataset comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages ranging from low to high-resource, for many of which no public dataset is currently available. XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation. We fine-tune mT5, a state-of-theart pretrained multilingual model, with XL-Sum and experiment on multilingual and lowresource summarization tasks. XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training. Additionally, training on low-resource languages individually also provides competitive performance. To the best of our knowledge, XL-Sum is the largest abstractive summarization dataset in terms of the number of samples collected from a single source and the number of languages covered. We are releasing our dataset and models to encourage future research on multilingual abstractive summarization. The resources can be found at https://github. com/csebuetnlp/xl-sum.

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“…However, the above methods also require a large amount of demonstration data, and cannot address the problem of multi-modal motion sequence encoding. Because it is unrealistic and expensive to obtain a large number of skill data of automatic assembly [28], the HMM based on a small number of samples training is feasible. In terms of data modeling for motion-oriented PBD, Hidden Markov Model is a general method of processing time series data in the field of robot PBD [29], [30], [31].…”

Section: B Task-oriented Programming By Demonstrationmentioning

confidence: 99%

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

et al. 2021

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To improve the programming efficiency of automatic assembly system, a novel skill programming framework based on task learning is proposed for modular assembly system in this paper. In this framework, the motion sequence of assembly skills can be modeled by demonstration data. And the assembly task is represented hierarchically. A complete assembly process of a part is divided into several skills, and each skill is divided into several sequential assembly motion primitives (AMP) of multiple modules. Then, a learning method of assembly motion sequence based on Hidden Markov Model is proposed, and the maximum probability method is used to generate the optimal sequential AMP. Each AMP is input to the assembly system in the form of instruction to complete the assembly. Aiming at the problem of accurate positioning and trajectory planning, visual guidance and direct teaching method are used to settle this problem. To evaluate the viability of the proposed framework, a customized modular assembly system is used to acquire the demonstration data, and a graphical user interface (GUI) software is designed. Five assembly skills are learned. Experimental are conducted to validate the effectiveness of the proposed method.

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Integrating Machine Learning with Human Knowledge

Cited by 129 publications

References 195 publications

Machine learning toward advanced energy storage devices and systems

Machine learning toward advanced energy storage devices and systems

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

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