Lastly, we demonstrate the superiority of stochastic proximal methods compared to subgradient-based approaches via extensive experiments.

The temporal trajectories of the variables of the learning problem are modeled by the so called 4th order Cognitive Action Laws (CALs) that come from stationarity conditions of a functional, as it happens for generalized coordinates in classical mechanics.

While much of MIP research focuses on designing effective heuristics, the question of how to manage multiple MIP heuristics in a solver has not received equal attention.

We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architecture to different tasks.

We then propose two methods to mitigate this problem.

ImageNet-100 labels, to define the positive samples. In Figure 1, we track the proxy task performance when progressively moving from MoCo to MoCo-v2, i.e . Figure 1 are for the same τ = 0 .2 . B.2 Hard negative mixing variants not discussed in the main text While developing MoCHi, we considered a number of different mixing strategies in feature space. We found the two strategies presented in Sections 4.1 and 4.2 of the main paper to For MoCHi, the "top" negatives are defined via the negative For MoCHi, in Section 4.2 we propose to synthesize MoCHi samples according to the percentage of the query they have.

We investigate the multiscale extension of the problem where the loss ranges of the experts are vastly different.

We study long-run market stability for repeated price competitions between two firms, where consumer demand depends on firms' posted prices and consumers'