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Extracting Data at Scale: Machine learning at the Natural History Museum
2021
Biodiversity Information Science and Standards
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Machine learning applications can deliver a step-change in our activities' scope, scale, and speed (Borsch et al. 2020). ...
This presentation will give an overview of these projects, and our future plans for using ML to extract data at scale within the Natural History Museum. ...
Machine learning applications can deliver a step-change in our activities' scope, scale, and speed (Borsch et al. 2020 ). ...
doi:10.3897/biss.5.74031
fatcat:o4xkzf4ftjg4pcklu3g7jilfdi
Deep Learning Frameworks In High Performance Computing Environments
2018
Zenodo
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and large-scale data. ...
However, there is no integrated research that offers insights into the deep learning frameworks that crunch the big data at scale. ...
doi:10.5281/zenodo.1186971
fatcat:eggksvlahzfd3inqj3zwats3yi
Accelerating skills development in Data science and AI at scale
2020
Zenodo
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At the Monash Data Science and AI platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in ...
In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. ...
researchers with large and
complex datasets to apply data science, Machine
Learning and AI to their research
• Underpin the Monash Data Futures Institute,
translating
discoveries
into
life-changing ...
doi:10.5281/zenodo.4287745
fatcat:k22bsbadlffqlhiwj3fiuaowwu
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
2019
npj Digital Medicine
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The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this ...
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. ...
Theory-driven machine learning can yield data-efficient workflows for predictive modeling by synthesizing prior knowledge and multimodality data at different scales. ...
doi:10.1038/s41746-019-0193-y
pmid:31799423
pmcid:PMC6877584
fatcat:uhgdhq7rjffqnboydb3e6d2tuu
Second International Workshop on Data-Centric Dependability and Security (DCDS)
2020
Dependable Systems and Networks
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This calls for solutions combining the latest advances in areas such as large-scale data processing, data science, visualization and machine and statistical learning. ...
It aims to discuss novel approaches for processing and analysing data generated by the systems as well as information gathered from open sources, leveraging from data science, machine and statistical learning ...
This calls for solutions combining the latest advances in areas such as large-scale data processing, data science, visualization and machine and statistical learning. ...
doi:10.1109/dsn-w50199.2020.00007
dblp:conf/dsn/MedeirosGKF20
fatcat:rmvpyxjgxfahzesaxqnhzos4b4
How the Public Clouds are Innovating on AI
2022
Compunet ( The Egyptian Information Journal )
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to develop, test, and deploy machine learning models on their clouds. ...
How the Public Clouds are Innovating on AI Thinkstock The three big cloud providers, specifically Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), want developers and data scientists ...
Battle of the AI services Most data science teams won't be developing massive-scale AI but do want to create and configure advanced machine learning models. ...
doi:10.21608/jstc.2022.252012
fatcat:t7twjdwdfbhs5ktvzpywmmioga
Big Data Analytics using Meta Machine Learning
English
2014
International Journal of Innovative Research in Science Engineering and Technology
English
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As "Big Data" grows bigger at a rapid speed, Machine Learning (MACHINE LEARNING) techniques have come to play a vital role in automatic data processing and analytics across a wide spectrum of application ...
We wish to refer to this science as "Meta-Machine Learning" (MMACHINE LEARNING). ...
One idea that can be explore is the primary mapping between large-scale data streams and learning algorithms. ...
doi:10.15680/ijirset.2014.0308072
fatcat:3qwqpolrerf6jj5c77yqpwdoee
Machine Learning and Ecosystem Informatics: Challenges and Opportunities
[chapter]
2009
Lecture Notes in Computer Science
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This talk will discuss the ways in which machine learning-in combination with novel sensors-can help transform the ecosystem sciences from small-scale hypothesis-driven science to global-scale data-driven ...
The talk will also discuss the recent development of The Evidence Tree Methodology for complex machine learning applications. ...
At the other extreme-which we might call "science-in-the-large"-automated instruments collect massive amounts of observational data, which are then analyzed via machine learning and data mining algorithms ...
doi:10.1007/978-3-642-05224-8_1
fatcat:4z7jjud7pjavrnee33ctkobgvq
Artificial Intelligence Integration with Nanotechnology
2020
Nanotechnology & Applications
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In this short communication, we present such innovative and dynamic sites utilizing artificial intelligence and its sub-sets of machine learning driven by deep learning in Nanotechnology. ...
AI has predictive power based on its data analytic functionality and some levels of autonomous learning, which its raw ingredient is just the massive sheer volume of data. ...
data at the speed of your computer Central Processing Unit (CPU), digital at scale and speed (i.e., nanotechnology), optimizing your business. ...
doi:10.33425/2639-9466.1025
fatcat:nqw52lett5d4hbdfiu7yam46ce
Third International Workshop on Data-Centric Dependability and Security (DCDS)
2021
2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)
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This calls for solutions combining the latest advances in areas such as large-scale data processing, data science, visualization and machine and statistical learning. ...
It aims to discuss novel approaches for processing and analysing data generated by the systems and information gathered from open sources, leveraging from data science, machine and statistical learning ...
This calls for solutions combining the latest advances in areas such as large-scale data processing, data science, visualization and machine and statistical learning. ...
doi:10.1109/dsn-w52860.2021.00006
fatcat:ktd7ecfssfftvcbb7feqxnrbja
Message from the DCDS 2019 Organizers
2019
2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)
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This calls for solutions combining the latest advances in areas such as large-scale data processing, data science, visualization and machine and statistical learning. ...
It aims to discuss novel approaches for processing and analysing data generated by the systems as well as information gathered from open sources, leveraging from data science, machine and statistical learning ...
Moreover, we would like to thank the organizers of the DSN conference for their help and support of the DCDS workshop and the community for their valued contributions to the workshop. ...
doi:10.1109/dsn-w.2019.00006
fatcat:hlktiqqo55dvdkdifj7bqfqplu
CANFAR+Skytree: A Cloud Computing and Data Mining System for Astronomy
[article]
2013
arXiv
pre-print
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Because Skytree scales to large data in linear runtime, this allows the full sophistication of the huge fields of data mining and machine learning to be applied to the hundreds of millions of objects that ...
At the Canadian Astronomy Data Centre, we have combined our cloud computing system, CANFAR, with the world's most advanced machine learning software, Skytree, to create the world's first cloud computing ...
Gray and M. Hack for their contributions to this work. ...
arXiv:1312.3996v1
fatcat:mhm6ffqi4vhkhpekhz5ujdcddu
Scientific Machine Learning Benchmarks
[article]
2021
arXiv
pre-print
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With a new generation of experimental facilities, the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. ...
In the context of science, scientific machine learning focuses on training machines to identify patterns, trends, and anomalies to extract meaningful scientific insights from such datasets. ...
Acknowledgements: We would like to thank Samuel Jackson, Kuangdai Leng, Keith Butler and Juri Papay from the Scientific Machine Learning Research Group at the Rutherford Appleton Laboratory, Junqi Yin ...
arXiv:2110.12773v1
fatcat:xrrwsb25jva5nab2t5qxgqtj6a
Automated Data Analysis Strategy for Synchrotron Experiments
2017
International Journal of Applied Science - Research and Review
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and time-scales. ...
And there are lots of existing data which can be used to train machine based systems. In all, Machine Learning has the potential to become an important component of materials research. ...
doi:10.21767/2394-9988.100057
fatcat:vwgxjynwk5fyjjsksiqannsjdi
CONVERGENCE OF HPC AND AI: TWO DIRECTIONS OF CONNECTION
2018
Azerbaijan Journal of High Performance Computing
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The findings from the literature review illustrate how these two main fields of science and information technologies can be converged together for the achievement of the goals in designing and developing ...
Reviewing of large-scale data, mining of significant and unknown knowledge and facts from the enormous amount of information are the challenging problems of machine learning and big data. ...
Applications of machine learning algorithms in big data Big data analytics, extraction of useful knowledge from the massive scale of information, causal inference through the texts, text summarization, ...
doi:10.32010/26166127.2018.1.2.179.184
fatcat:vhxbfrgtcfhi3m2xjqdewtu4aq
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