PREDICTING THROUGH AUTOMATED REASONING: A DISRUPTIVE CYCLE OF ENHANCED AND ATTAINABLE NEURAL NETWORK ARCHITECTURES

Machine learning has advanced considerably in recent years, with algorithms achieving human-level performance in various tasks. However, the true difficulty lies not just in training these models, but in implementing them efficiently in everyday use cases. This is where machine learning inference becomes crucial, surfacing as a key area for researc