Exact Gauss-Newton Optimization for Training Deep Neural Networks
We present EGN, a stochastic second-order optimization algorithm that combines the
generalized Gauss-Newton (GN) Hessian approximation with low-rank linear algebra to …
generalized Gauss-Newton (GN) Hessian approximation with low-rank linear algebra to …
[图书][B] Fast Training of Generalizable Deep Neural Networks
OB Pooladzandi - 2023 - search.proquest.com
Effective natural agents excel in learning representations of our world and efficiently
generalizing to make decisions. Critically, developing such advanced reasoning capabilities …
generalizing to make decisions. Critically, developing such advanced reasoning capabilities …
[PDF][PDF] Confidence Intervals in Machine Learning based Time Series Forecasts with Application to Demand Prediction
F Kurth - 2024 - eplus.uni-salzburg.at
This thesis focuses on presenting and evaluating different methods for constructing
predictive confidence intervals in neural network models, with a focus on their application in …
predictive confidence intervals in neural network models, with a focus on their application in …
Analysis of Player Performance and Game Fluctuation Based on LM-BP Neural Network and SVM
W Li, K Lin, Z Fan - 2024 IEEE 2nd International Conference on …, 2024 - ieeexplore.ieee.org
In this study, the 2023 Wimbledon Men's Singles Final was used as the object of study, and
the intense match between Carlos Alcaraz and Novak Djokovic was comprehensively …
the intense match between Carlos Alcaraz and Novak Djokovic was comprehensively …