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DLHub: Model and Data Serving for Science [article]

Ryan Chard, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steve Tuecke, Ben Blaiszik, Michael J. Franklin, Ian Foster
2018 arXiv   pre-print
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]

Ben Blaiszik, Logan Ward, Marcus Schwarting, Jonathon Gaff, Ryan Chard, Daniel Pike, Kyle Chard, Ian Foster
2019 arXiv   pre-print
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

Lauri Himanen, Amber Geurts, Adam Stuart Foster, Patrick Rinke
2019 Advanced Science  
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

Zhi Hong, J. Gregory Pauloski, Logan Ward, Kyle Chard, Ben Blaiszik, Ian Foster
2021 Frontiers in Molecular Biosciences  
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]

Lixi Zhou, Arindam Jain, Zijie Wang, Amitabh Das, Yingzhen Yang, Jia Zou
2022 arXiv   pre-print
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]

Ryan Jacobs, Mingren Shen, Yuhan Liu, Wei Hao, Xiaoshan Li, Ruoyu He, Jacob RC Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G. Field (+1 others)
2021 arXiv   pre-print
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

Jiaru Bai, Liwei Cao, Sebastian Mosbach, Jethro Akroyd, Alexei Lapkin, Markus Kraft, Apollo-University Of Cambridge Repository
2022
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