Europe
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.
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation Marco Bellagente 4 Manuel Brack 2, 3 Hannah Teufel 1 Felix Friedrich
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users.
OptimalRatesforVector-ValuedSpectral RegularizationLearningAlgorithms
We study theoretical properties of a broad class of regularized algorithms with vector-valued output.
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.