PREDICTING THROUGH AUTOMATED REASONING: THE PINNACLE OF TRANSFORMATION FOR STREAMLINED AND REACHABLE NEURAL NETWORK ARCHITECTURES

Machine learning has advanced considerably in recent years, with algorithms surpassing human abilities in various tasks. However, the main hurdle lies not just in developing these models, but in utilizing them efficiently in everyday use cases. This is where machine learning inference comes into play, arising as a primary concern for scientists and