Asia
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.
Learning From Biased Soft Labels Hua Yuan 1,2, Y u Shi
The proposed indicators adequately measure the effectiveness of the soft labels generated in this process.
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training Y anlai Y ang 1, Matt Jones
The behavior emerges and becomes more robust as the architecture scales up its number of parameters.
Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantization
Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks by constraining their weights to a limited set of values.
Unveiling
Earlier research highlighted DMs' vulnerability todatapoisoning attacks, butthese studies placed stricter requirements than conventional methods like'BadNets' inimage classification.
