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 tech.preferred.jp
Test-time adaptation for brain tumor segmentation with cross-institutional MRI - Preferred Networks Research & Development
| | resources.paperdigest.org
Most Influential NIPS Papers (2021-08) - Resources | Paper Digest
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| | The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyze all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically construc
| | blog.ml.cmu.edu
Regret Circuits: Composability of Regret Minimizers - Machine Learning Blog | ML@CMU | Carnegie Mellon University
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| | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University.
| | anitagraser.com
Movement data in GIS and the AI hype | Free and Open Source GIS Ramblings
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| | This post looks into the current AI hype and how it relates to geoinformatics in general and movement data analysis in GIS in particular. This is not an exhaustive review but aims to highlight some of the development within these fields. There are a lot of references in this post, including some to previous work...
| | blog.fastforwardlabs.com
"Hello world" in Keras (or, Scikit-learn versus Keras)
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| This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.