Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities

Xin, Doris; Ma, Litian; Liu, Jialin; Macke, Stephen; Song, Shuchen; Parameswaran, Aditya

doi:10.48550/arxiv.1804.05892

Cited by 2 publications

(2 citation statements)

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“…Moreover, studies have attempted to integrate ML and scientific workflow [43]. A recent study [44] attempted to shorten the time to obtain deployable scientific machine learning models from scratch. The automation of iterative processes, including building, testing, and refining models, can eliminate the need for manually performing such repetitive and lengthy tasks.…”

Section: Application Of MLmentioning

confidence: 99%

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

Nouri¹,

Davis²,

Subedi³

et al. 2021

Preprint

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In this survey, we discuss the challenges of executing scientific workflows as well as existing Machine Learning (ML) techniques to alleviate those challenges. We provide the context and motivation for applying ML to each step of the execution of these workflows. Furthermore, we provide recommendations on how to extend ML techniques to unresolved challenges in the execution of scientific workflows. Moreover, we discuss the possibility of using ML techniques for in-situ operations. We explore the challenges of in-situ workflows and provide suggestions for improving the performance of their execution using ML techniques.

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Section: Application Of MLmentioning

confidence: 99%

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

Nouri¹,

Davis²,

Subedi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This is made possible by the interpretable nature of the output produced by symbolic learning. Although such cases are few in number, they is an important step towards building robust "human-in-the-loop" systems ( [29]) that use human expertise to enhance their performance.…”

Section: Introductionmentioning

confidence: 99%

One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis

Sunder,

Srinivasan,

Vig

et al. 2019

Preprint

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Our interest in this paper is in meeting a rapidly growing industrial demand for information extraction from images of documents such as invoices, bills, receipts etc. In practice users are able to provide a very small number of example images labeled with the information that needs to be extracted. We adopt a novel 'two-level''neurodeductive', approach where (a) we use pre-trained deep neural networks to populate a relational database with facts about each document-image; and (b) we use a form of deductive reasoning, related to meta-interpretive learning of transition systems to learn extraction programs: Given task-specific transitions defined using the entities and relations identified by the neural detectors and a small number of instances (usually 1, sometimes 2) of images and the desired outputs, a resource-bounded meta-interpreter constructs proofs for the instance(s) via logical deduction; a set of logic programs that extract each desired entity is easily synthesized from such proofs. In most cases a single training example together with a noisy-clone of itself suffices to learn a program-set that generalizes well on test documents, at which time the value of each entity is determined by a majority vote across its program-set. We demonstrate our two-level neuro-deductive approach on publicly available datasets ("Patent" and "Doctor's Bills") and also describe its use in a real-life industrial problem.

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Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities

Cited by 2 publications

References 10 publications

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis

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