However, recent observations show that manyVQAmodels mayonlycapture thebiasesbetweenquestions andanswers in a dataset rather than showing real reasoning abilities.

This issuboptimal because the classifier isinherently biased towards a subset of the modalities. To alleviate this shortcoming, we propose a novel regularization term based on the functional entropy.

Thus, K lar N1/3 problem, 3.2 Bounding Equation (3) indicates ofthe usual reward L. bestint O(1 rewards d and isofO(T).

The way to instantiate BACON will be similar to MFN. The following Lemma will showthat Definition 1.2 can be extended toanalyzing functions from differentdomain. Let F = gL g1 γ, with gi being a multivariate polynomial. The inductive hypothesis is: fork 1, if zk[j] is linear sum ofB for all j, then zk+1[l]islinearsumsofB foralll. By definition ofz, we know thatzk+1 = gk(zk), where gk is a multivariate polynomial of finite degreed.

Neural fields have emerged as a new paradigm for representing signals, thanks to their ability to do it compactly while being easy to optimize. In most applications, however, neural fields are treated like black boxes, which precludes manysignal manipulation tasks.

Orthogonally, algorithm unrolling[14] implements iterations of a model-based algorithm as a sequence of neural layers to build afeed-forward network, whose parameters can be learned endto-end via back-propagation from data. A classic example is the unrolling of theiterative soft-1While works existtoanalyze existing transformer architectures [5,6,7,8,9],only [10,11]characterized the performance ofasingle self-attention layer and ashallowtransformer,respectively.

Efficient exploration in deep cooperativemulti-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.