SYSTEMS AND METHODS FOR RESOURCE EFFICIENT MODEL LEARNING AND MODEL INFERENCE

Presented herein are framework embodiments that allow the representation of complex systems and processes that are suitable for resource efficient machine learning and inference. Furthermore, disclosed are new reinforcement learning techniques that are capable of learning to plan and optimize dynamic and nuanced systems and processes. Different embodiments comprising combinations of one or more neural networks, reinforcement learning, and linear programming are discussed to learn representations and models—even for complex systems and methods. Furthermore, the introduction of neural field embodiments and methods to compute a Deep Argmax, as well to invert neural networks and neural fields with linear programming, provide the ability to create models and train models that are accurate and very resource efficient—using less memory, less computations, less time, and, as a result, less energy. As a result, these models can be trained and re-trained quickly and efficiently; thereby not only using fewer resources but also providing models that are continually improving. The resource efficiencies herein also allow such models to be trained and/or deployed more widely.