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.
REDUCR: Robust Data Downsampling using Class Priority Reweighting
Neural Information Processing Systems
The abundance of data has had a profound impact on machine learning (ML), both positive and negative.
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CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Neural Information Processing Systems
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.
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Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training Y anlai Y ang 1, Matt Jones
Neural Information Processing Systems
The behavior emerges and becomes more robust as the architecture scales up its number of parameters.
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