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cs.LG2025
Exploring possible vector systems for faster training of neural networks with preconfigured latent spaces
Nikita Gabdullin
The overall neural network (NN) performance is closely related to the properties of its embedding distribution in latent space (LS). It has recently been shown that predefined vect…
cs.LG2025
Using predefined vector systems as latent space configuration for neural network supervised training on data with arbitrarily large number of classes
Nikita Gabdullin
Supervised learning (SL) methods are indispensable for neural network (NN) training used to perform classification tasks. While resulting in very high accuracy, SL training often r…
cs.LG2025
The effects of Hessian eigenvalue spectral density type on the applicability of Hessian analysis to generalization capability assessment of neural networks
Nikita Gabdullin
Hessians of neural network (NN) contain essential information about the curvature of NN loss landscapes which can be used to estimate NN generalization capabilities. We have previo…