Over the last several decades, computational neuroscience researchers have proposed a variety of "biologically plausible" models of synaptic plasticity that seek to provide normative accounts of a variety of learning processes in the brain--these models aim to explain how modifications of

Instance segmentation, which seeks to obtain both class and instance labels for each pixelinthe input image, isachallenging task incomputer vision. State-ofthe-art algorithms often employ a search-based strategy, which first divides the output image with a regular grid and generate proposals at each grid cell, then the proposals are classified and boundaries refined.

Taking advantage of an analogy between Transformer stages and the evolution of a dynamical system of multiple interacting particles, we formulate a temporal evolution scheme,TransEvolve, to bypass costly dot-product attention over multiple stacked layers.

Here, we focus ourattention onthesecond case, which leads toscore improvement without anyre-training.

This paper studies minimax optimization problemsminxmaxyf(x,y), where f(x,y) is mx-strongly convex with respect tox, my-strongly concave with respect to y and (Lx,Lxy,Ly)-smooth. Zhang et al. [42] provided the following lower bound of the gradient complexity for any first-order method: Ω q