Advanced Neural Network Optimization Techniques for Large-Scale Machine Learning

Authors: Dr. Academic Name, Jane Researcher, Prof. Senior Collaborator
Published in: Proceedings of the International Conference on Machine Learning (ICML) (2024)
Date:
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Abstract

We present novel optimization techniques for training large-scale neural networks that significantly reduce computational overhead while maintaining model performance. Our approach introduces adaptive learning rate scheduling combined with gradient compression methods, resulting in 40% faster training times and improved convergence stability.

Keywords

neural networks, optimization, machine learning, gradient descent

Details

Advanced Neural Network Optimization Techniques for Large-Scale Machine Learning

Overview

This paper addresses the computational challenges in training large-scale neural networks by proposing novel optimization techniques that balance performance with efficiency. Our work demonstrates significant improvements in training speed while maintaining model accuracy across diverse applications.

Key Contributions

  • Novel adaptive learning rate scheduling algorithm
  • Gradient compression method with theoretical guarantees
  • Comprehensive evaluation on large-scale datasets
  • Open-source implementation for reproducibility

Methodology

Our approach combines two key innovations:

  1. Adaptive Learning Rate Scheduling: A dynamic scheduling algorithm that adjusts learning rates based on gradient magnitude and training progress
  2. Gradient Compression: A lossy compression technique that reduces communication overhead in distributed training

Results

Experimental evaluation on ImageNet, CIFAR-100, and custom large-scale datasets shows:

  • 40% reduction in training time
  • Maintained accuracy within 0.5% of baseline models
  • Improved convergence stability across different architectures
  • Scalability to 1000+ GPU clusters

Impact

This work has been cited by subsequent research in distributed machine learning and has influenced optimization strategies in major deep learning frameworks. The open-source implementation has been adopted by several industry practitioners.

Citation

BibTeX 
@inproceedings{academic2024neural,
  title={Advanced Neural Network Optimization Techniques for Large-Scale Machine Learning},
  author={Academic, Dr. and Researcher, Jane and Collaborator, Prof. Senior},
  booktitle={Proceedings of the International Conference on Machine Learning},
  pages={1234--1245},
  year={2024},
  organization={ICML}
}