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research.google | ||
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ehudreiter.com
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| | | | | I dont like academic leaderboards. Poor scientific techniques, poor data, and poor evaluation means leaderboard results may not be worth much. I also suspect that the community's fixation on leaderboards also means less research on important topics that do not fit the leaderboard model, such as understanding user requirements. | |
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blog.fastforwardlabs.com
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| | | | | Graph Neural Networks (GNNs) are neural networks that take graphs as inputs. These models operate on the relational information in data to produce insights not possible in other neural network architectures and algorithms. While there is much excitement in the deep learning community around GNNs, in industry circles, this is sometimes less so. So, I'll review a few exciting applications empowered by GNNs. Overview of Graphs and GNNs A graph (sometimes called a network) is a data structure that highlights the relationships between components in the data. | |
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www.assemblyai.com
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| | | | | Let's discover how this cutting-edge technology is powering production applications and may be shaping the future of AI. | |
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moscardino.net
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| | | A look at React Native and Ionic Framework. | ||
