Through a deeper understanding of predictions of neural networks, Influence Function (IF) has been applied to various tasks such as detecting and relabeling mislabeled samples, dataset pruning, and separation of data sources in practice.

F s,iBs,i, n = 1,2,...,S, (10) where F s is the support features extracted by a pretrained ViT. Inspired by the multiple-object tracking within a single framework [21], in which different objects are represented by various identifications (i.e., learnable vectors) for simultaneously tracking, we add extra learnable tokens tothemeanfeatures formorediscriminativeprompts.

The pretraining data of today's strongest language models is opaque; in particular,

Zhang et al. have recently shown that geodesic convex concave Riemannian problems always admit saddle-point solutions.

ConsiderK arms whose reward distributions (ν1,...,νK) come from a one-dimensional exponential family and are of unknown means µ=(µ1,...,µK).

The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle, or end-effector position. This forces the agent to make decision at each point in training, and hence, limit the scalability to continuous, high-dimensional,andlong-horizontasks.Incontrast,researchinclassicalrobotics has, for a long time, exploited dynamical systems as a policy representation to learn robot behaviors via demonstrations.