Let (M, g) bead-dimensional Riemannianmanifold.

Moreover, Evolution Gym is easy to use.

Our second contribution is to introduce ensemble of clustered desparsified multi-task Lasso (ecd-MTLasso), which has two advantages compared to current methods:i)it offers statistical guarantees andii)it allows to trade spatial specificity for sensitivity, leading to a powerful adaptive method. Our third contribution is an empirical validation of the theoretical claims.

The goal of zero-shot human-AI coordination is to develop an agent capable of collaborating with humans without relying on human data.

However, the current MIL methods are usually based on independent and identical distribution hypothesis, thus neglect the correlation among different instances. To address this problem, we proposed a new framework, called correlated MIL, and provided a proof for convergence. Based on this framework, we devised a Transformer based MIL (TransMIL), which explored both morphological and spatial information. The proposed TransMIL can effectively deal with unbalanced/balanced and binary/multiple classification with great visualization and interpretability.

The nature of structured models is that communication among the components has a bottleneck, typically achieved by restricted connectivity and attention.