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DLHub: Model and Data Serving for Science
[article]
2018
arXiv
pre-print
Preserved Fulltext
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. ...
We also describe early uses of DLHub for scientific applications. ...
ACKNOWLEDGMENTS This work was supported in part by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory and the RAMSES project, both from the U.S. ...
arXiv:1811.11213v1
fatcat:zmmandowgnco7n3b5rqd3nao5q
A Data Ecosystem to Support Machine Learning in Materials Science
[article]
2019
arXiv
pre-print
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We use examples to show how MDF and DLHub capabilities can be leveraged to link data with machine learning models and how users can access those capabilities through web and programmatic interfaces. ...
of new data across the ecosystem, and the connecting of data with materials-specific machine learning models. ...
and reuse; software tools to simplify data discovery, aggregation and use; and a library of curated machine learning models and processing logic that can easily be applied to new data streams. ...
arXiv:1904.10423v1
fatcat:gibssrxzxbaqxdjsfa4ld4gd2a
Data‐Driven Materials Science: Status, Challenges, and Perspectives
2019
Advanced Science
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Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. ...
In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures ...
Rousi, Milica Todorovic ′, Sven Bossuyt, Miguel Caro, David Gao, Matthias Scheffler, Bryce Meredig, and Heidi Henrickson for insightful discussions and a careful reading of our manuscript. Computing ...
doi:10.1002/advs.201900808
pmid:31728276
pmcid:PMC6839624
fatcat:j6adrk22zfadrm4usoelg3c7p4
Models and Processes to Extract Drug-like Molecules From Natural Language Text
2021
Frontiers in Molecular Biosciences
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We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to ...
We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling ...
DLHub: Simplifying Publication, Discovery, and Use of Machine Learning Models in Science. J. ...
doi:10.3389/fmolb.2021.636077
pmid:34527701
pmcid:PMC8435623
fatcat:px7vzgcu7vgatousxdtnusuktu
Benchmark of DNN Model Search at Deployment Time
[article]
2022
arXiv
pre-print
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Deep learning has become the most popular direction in machine learning and artificial intelligence. ...
However, the preparation of training data, as well as model training, are often time-consuming and become the bottleneck of the end-to-end machine learning lifecycle. ...
Bob wants to create a machine learning model that predicts the downtimes for a specific machine type. ...
arXiv:2206.00188v1
fatcat:3tawlkzhwzebhbojr76f6uuuze
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
[article]
2021
arXiv
pre-print
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In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask ...
To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests which include a suite of random splits, and dataset size-dependent and domain-targeted ...
This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science ...
arXiv:2110.08244v1
fatcat:s7qaovfr6jf5vkqdkolmp5cbra
From Platform to Knowledge Graph: Evolution of Laboratory Automation
2022
Preserved Fulltext
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Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. ...
The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. ...
A relevant first step toward addressing this issue is demonstrated by DLHub, 152 which allows users to publish, share, and cite ML models for applications in science. ...
doi:10.17863/cam.83288
fatcat:igsnqxp52fhlxowgcbck7ub2xu