The negative prompt used is " unrealistic, bad anatomy, wrong anatomy, extra limb, missing limb,

Reasoning over TKGs has two challenges: 1.

He Did PR for Zuckerberg, Musk, and Google. Now He Says He'Only Told Half the Story' Thirty thousand feet in the air, Mark Zuckerberg turned to his speechwriter. The duo were flying in Zuckerberg's jet to the United Nations General Assembly in New York, where the Facebook boss was scheduled to address world leaders. Zuckerberg had a question for his companion. "Wait, what exactly is the UN?" Dex Hunter-Torricke had to hide his surprise. Zuckerberg was, by this point in 2015, the head of a company that was reshaping politics and societies around the world, with 1.5 billion users and counting.

Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.

Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.

A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses.

In this paper, we focus on computing an NE that optimizes a given objective function.