master_thesis's Introduction
master_thesis's People
master_thesis's Issues
Finish the chapters about modern stochastic optimization algorithms
- use paper Second-Order Stochastic Optimization for Machine Learning in Linear Time
- find and use papers about stochastic L-BFGS
Create the first motivational experiment
- uses paper 1
- uses implementation of a Maxout network
Create other motivational experiments
- uses paper 1
- uses implementation of an adversarially trained maxout network, ReLU network with dropout, ReLU network without dropout, Sigmoid network, Convolutional network, MP-DBM, Dropout LSTM
Finish the chapter about the main paper
- use and describe paper Stochastic Cubic Regularization for Fast Nonconvex Optimization
- reproduce the experiments
Adapt the code for the inner problem of the cubic regularizer
- use and describe the paper Adaptive gradient descent without descent.
Implement the algorithm of the main paper
- Implement the algorithm
- Test on the used datasets
- Use some other datasets
Document the motivational experiments in LaTeX
Review the first paper in LaTeX
- finish the chapter about the paper Cubic regularization of Newton method and its global performance
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