Deep Learning
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.
RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy Hongting Y e
Recently, extensive literature has shown that the relationship between SC and FC is complex and not a simple one-to-one mapping.
Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View Zhiyu Lin
A widely discussed hypothesis regarding the cause of visual models' lack of robustness is that they can exploit human-imperceptible high-frequency components
