Tuning parameters of deep neural network training algorithms pays off: a computational study
From MaRDI portal
Publication:6635854
DOI10.1007/s11750-024-00683-xMaRDI QIDQ6635854
Lorenzo Papa, Laura Palagi, Irene Amerini, Marco Boresta, Corrado Coppola
Publication date: 12 November 2024
Published in: Top (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Applications of mathematical programming (90C90)
Cites Work
- Unnamed Item
- Unnamed Item
- Support vector machines maximizing geometric margins for multi-class classification
- Mathematical optimization in classification and regression trees
- On the limited memory BFGS method for large scale optimization
- Machine learning models and algorithms for big data classification. Thinking with examples for effective learning
- A comparative study of the leading machine learning techniques and two new optimization algorithms
- Optimization problems for machine learning: a survey
- High-dimensional dynamics of generalization error in neural networks
- First-order and stochastic optimization methods for machine learning
- Global optimization issues in deep network regression: an overview
- Support Vector Machines
- Numerical Optimization
- Gradient Convergence in Gradient methods with Errors
- Optimization Methods for Large-Scale Machine Learning
- A robust multi-batch L-BFGS method for machine learning
- Data Mining and Knowledge Discovery Handbook
- Benchmarking Derivative-Free Optimization Algorithms
- A Stochastic Approximation Method
- Suboptimal Local Minima Exist for Wide Neural Networks with Smooth Activations
- A jamming transition from under- to over-parametrization affects generalization in deep learning
- Optimal classification trees
- A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
- Benchmarking optimization software with performance profiles.
Related Items (1)
This page was built for publication: Tuning parameters of deep neural network training algorithms pays off: a computational study