Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Adversarial Learning for Feature Shift Detection and Correction
Neural Information Processing Systems
Distribution shifts in multi-dimensional data, caused by one or more "corrupted" dimensions, are
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Graph Neural Networks Need Cluster-Normalize-Activate Modules
Neural Information Processing Systems
Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data.
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MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering Yizhen Luo
Neural Information Processing Systems
Studying protein mutations within amino acid sequences holds tremendous significance in life sciences.
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