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 deepmind.google
Millions of new materials discovered with deep learning - Google DeepMind
| | www.simula.no
Enabling Graph Neural Networks at Exascale | Simula
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| | Graph neural networks (GNNs), which extend the successful ideas of deep learning to irregularly structured data, are a recent addition to the field of artificial intelligence. While traditional deep learning has focused on regular inputs such as images composed of pixels in two-dimensional space, graph neural networks can analyze and learn from unstructured connections between objects. This gives GNNs the ability to tackle completely new classes of problems, such as analyzing social networks and power grids or uncovering molecule structures in computational chemistry.
| | research.google
Robust Graph Neural Networks
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| | Posted by Bryan Perozzi, Research Scientist and Qi Zhu, Research Intern, Google Research Graph Neural Networks (GNNs) are powerful tools for levera...
| | futurism.com
Scientists Demonstrate "Liquid Light" at Room Temperature for the First Time
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| | The work paves the way for studying quantum hydrodynamics further and for future applications of this new type of matter in electronics devices.
| | blog.vstelt.dev
Writing a Neural Network from Scratch in Rust
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| [AI summary] The article explains the process of building a neural network from scratch in Rust, covering forward and backward propagation, matrix operations, and code implementation.