Stochastic Gradient Optimization Techniques

Stochastic Gradient Optimization Techniques is a research topic within Artificial Intelligence. Science Explorer counts 7.9k research works in it since 1965. 10.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of optimization methods in machine learning, particularly in the context of stochastic gradient descent, random projections, deep learning, convex optimization, matrix decompositions, and large-scale optimization. The papers explore various algorithms and techniques for improving the efficiency and effectiveness of machine learning models, with a specific emphasis on neural networks and generalization.

• Stochastic Gradient Descent
• Random Projections
• Deep Learning
• Convex Optimization
• Matrix Decompositions
• Approximation Algorithms
• Large-Scale Optimization
• Neural Networks
• Coordinate Descent
• Generalization