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Robust Graph Neural Networks
| | www.unofficialgoogledatascience.com
Using random effects models in prediction problems
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| | by NICHOLAS A. JOHNSON, ALAN ZHAO, KAI YANG, SHENG WU, FRANK O. KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction ...
| | blog.research.google
Re-weighted gradient descent via distributionally robust optimization - Google Research Blog
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| | [AI summary] This blog post introduces Stochastic Re-weighted Gradient Descent (RGD), a novel optimization algorithm that improves deep neural network performance by re-weighting data points during training based on their difficulty, enhancing generalization and robustness against data distribution shifts.
| | www.blopig.com
Protein Property Prediction Using Graph Neural Networks | Oxford Protein Informatics Group
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| | [AI summary] The article discusses the application of graph neural networks (GNNs) in protein property prediction, highlighting their ability to model protein structures and interactions, the integration of pre-trained protein language models like ESM, and the use of residual layers to address oversmoothing challenges.
| | blog.ml.cmu.edu
How to Regularize Your Regression - 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.