Demystifying the interactions among multiple agents from their past trajectories is
fundamental to precise and interpretable trajectory prediction. However, previous works only …

To realize trajectory prediction, most previous methods adopt the parameter-based
approach, which encodes all the seen past-future instance pairs into model parameters …

The shuffled frog lea** algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …

Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …

The Internet of Things (IoT) has recently become important in accelerating various functions,
from manufacturing and business to healthcare and retail. A recommender system can …

In recent years, the rapid advancement of deep learning and the advent of extensive human
motion datasets have significantly enhanced the prominence of human motion prediction …

Demystifying the interactions among multiple agents from their past trajectories is
fundamental to precise and interpretable trajectory prediction. However, previous works …

Abstract Sine Cosine Algorithm (SCA), as a recently viral population-based meta-heuristic,
which is in the extensive application for a variety of optimization cases. Regardless of the …

The slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The
algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the …

The hunger games search (HGS) algorithm is a recently proposed population-based
optimization algorithm that mimics a common phenomenon of animals searching for food …