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Versions and GitHub

Releases Overview

This project has undergone multiple releases, each marking a significant advancement in its development. Below is a summary of the key versions:

Release 1 (R1) - 2023

  • Created as a master thesis at Politechnika Warszawska, Wydział Matematyki i Nauk Informatycznych
  • The initial version of the project was built directly from the master branch.
  • This version was developed as part of a thesis project and was not hosted in a GitHub repository.
  • Provided the foundation for dynamic neural network adaptation and Monte Carlo Tree Search (MCTS)-based optimization.

Release 2 (R2) - 2024

  • "Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search"
  • This version was derived from the codebase used for the ICCS 2024 paper:
  • Published in Computational Science – ICCS 2024 (Springer LNCS, vol. 14832)
  • Authors: Szymon Świderski & Agnieszka Jastrzębska
  • First Online: June 28, 2024
  • Pages: 362–377
  • Hosted on GitHub for improved version control and collaboration.
  • Code improvements for performance and stability, incorporating refinements based on ICCS 2024 research findings.

Release 3 (R3) - 2025

  • "Dynamic Neural Network with Matrix-Extended Residual Connections"
  • Completed with results published in ICCS 2025
  • Further optimizations and enhancements to the Monte Carlo Tree Search approach.
  • Improvements in dynamic network restructuring and efficiency.
  • Introduction of matrix-extended residual connections.

Release 4 (R4) - 2026 (Ongoing)

  • Current development version targeting ICCS 2026 submission
  • Feature to optimize already existing neural networks
  • Continued refinements in neural network architecture adaptation
  • Enhanced parameter reduction capabilities while maintaining accuracy

GitHub Repository Enhancements

With the migration to GitHub, several automated processes were introduced to improve the development workflow:

  • Automated Unit Tests: Each commit is automatically tested to ensure stability and correctness.
  • Performance Timing and Optimization Checks: Continuous benchmarking to track computational efficiency.
  • SonarQube Integration: Code quality and security analysis using SonarQube, ensuring maintainability and reducing technical debt.
  • CI/CD Pipeline: Implementation of automated builds and testing workflows.

Future Developments

  • Continued refinements in neural network architecture adaptation.
  • Exploration of additional reinforcement learning strategies for training efficiency.
  • Further integration of AI-driven code quality and performance analysis tools.

For more details and source code, visit the GitHub repository (link to be provided).