Revolutionizing Optimization and Deep Learning: A Thermodynamic Hybrid Network Inspired by the Nobel Prize in Physics 2024
DOI:
https://doi.org/10.70153/IJCMI/2024.16302Keywords:
Thermodynamic Hybrid Network, Artificial Neural Networks (ANNs),, Hopfield Network, Restricted Boltzmann Machines (RBMs), Energy Minimization, Stochastic Exploration, Optimization, Machine Learning, Deep Learning, Quantum-Inspired AlgorithmsAbstract
The 2024 Nobel Prize in Physics, awarded to John J. Hopfield and Geoffrey E. Hinton, recognized their foundational contributions to artificial neural networks (ANNs) and machine learning. This paper introduces a novel Thermodynamic Hybrid Network that merges Hopfield networks' energy minimization capabilities with the probabilistic dynamics of Restricted Boltzmann Machines (RBMs). Leveraging thermodynamic principles, this hybrid architecture addresses large-scale optimization challenges, such as the Traveling Salesman Problem and protein structure prediction, while overcoming the limitations of conventional ANNs. This approach bridges the gap between classical deterministic models and stochastic methods, opening the door to more efficient machine learning solutions across fields like computational biology, quantum computing, and materials science.
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