This phenomenon, that we coin "fast and furious" learning in games, sets a new benchmark about what is possible both in max-min optimization as well as in multi-agent systems.

Meta learning aims to learn the inductive biases ( e .

Wehere define the appearance as being the factors of data that are left after transformingxby its perspective.

Figure 2: These two graphs cannot be distinguished by 1-WL-test. The COMBINE step takes the result of AGGREGATE and the previous representation of current node asinput. Wereduce theFFN inner-layer dimension of4din [47] tod, which does not appreciably hurt the performance but significantly save the parameters. The embedding dropout ratio is set to 0.1 by default in many previous Transformer works[11,34]. The rest of hyper-parameters remain unchanged. Table 8 summarizes the hyper-parameters used for fine-tuning Graphormer on OGBGMolPCBA.

In this paper, we propose an efficient and effective training scheme coined as Sparse SAM (SSAM), which achieves sparse perturbation by a binary mask.

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Many existing approaches tend to assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients.

However,sincetheinitial large learning rate generally helps the optimizer to converge to flatter minima, we hypothesize that the winning tickets have relatively sharp minima, which is considered a disadvantage in terms of generalization ability.

We report top-1 center-crop accuracy on the validation split of ImageNet,Places205andiNaturalist18.

We show that the VC-Dimension of the model class which canrobustlyinterpolate any n samples is lower bounded byΩ(nd) where d is the dimension. In contrast, there are neural net architectures that can interpolaten samples with onlyO(n) parameters and VC-Dimension atmostO(nlogn).