ljk628/ML-Systems

papers on scalable and efficient machine learning systems

Fast and Scalable Machine Learning: Algorithms and Systems

This is a collection of papers about recent progress in machine learning and systems, including distributed machine learning, deep learning and etc.

Contents

1. Deep Learning
   • Convolutional Neural Networks
     • ImageNet Models
     • Architecture Design
     • Activation Functions
     • Visualization
     • Fast Convolution
     • Low-Rank Filter Approximation
     • Low Precision
     • Parameter Pruning
     • Transfer Learning
     • Theory
     • 3D Data
     • Hardware
   • Optimization for Deep Learning
     • Generalization
     • Loss Surface
     • Batch Size
     • General
     • Adaptive Gradient Methods
     • Distributed Optimization
     • Initialization
     • Low Precision
     • Normalization
     • Regularization
     • Meta Learning
   • Deep Learning Systems
     • General Frameworks
     • Specific System
     • Parallelization
2. Distributed Machine Learning
   • Distributed Optimization
   • Distributed ML Systems
3. Other Topics
   • Matrix Factorization
   • Graph Computation