Principled Neural Architecture Learning - Intel AI

A neural architecture, which is the structure and connectivity of the network, is typically either hand-crafted or searched by optimizing some specific objective criterion (e.g., classification accuracy). Since the space of all neural architectures is huge, search methods are usually heuristic and do not guarantee finding the optimal architecture, with respect to the objective criterion. In addition, these search methods might require a large number of supervised training iterations and use a high amount of computational resources, rendering the solution infeasible for many applications. Moreover, optimizing for a specific criterion might result in a model that is suboptimal for other useful criteria such as model size, representation of uncertainty and robustness to adversarial attacks. Thus, the resulting architectures of most strategies used today, whether hand crafting or heuristic searches, are densely connected networks, which are not an optimal solution for the objective they were created to achieve, let alone other objectives.