Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure.

General decision intelligence needs to solve tasks in many distinct domains.

However, these messages are past-oriented, and they do not consider the full KG context.

Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available.

They iteratively modify and resolve linear programs to conduct a depth-first search from any node in the search tree. Existing divers rely on generic decision rules that fail to exploit structural commonality between similar problem instances that often arise in practice.

It can also be seen as a local approximation of the nonconvex-nonconcave minimax games--e.g., the generative adversarial network (GAN)