More specifically,for the output features ofthe penultimate layer, for each class the within-class features converge to their means, and the means of different classes exhibit a certain tight frame structure, which is also aligned withthelastlayer'sclassifier.

Deep learning algorithms have shown significant development thanks tothelargepre-collected dataset, such as SQuAD [47]innatural language processing (NLP), and ImageNet [4] in computer vision.

A line of works (Yu et al., 2020; Wang et al., 2021; Fifty et al., 2021)resort performance droptooptimization challenge because conflicting tasksmight compete formodelcapacity.

The seeder generates as diversified candidate solutions as possible (seeds) while being dedicated to exploring over the full combinatorial action space (i.e.,sequence ofassignment action).

Thispaperproposes animplicit model-based multi-agent reinforcement learning method based onvalue decomposition methods. Under this method, agents can interact with thelearned virtual environment and evaluate thecurrent state value according to imagined future states in the latent space, making agents have the foresight. Our approach can be applied toanymulti-agent value decomposition method.

In this regard, we propose a novel memory mechanism that represents past events in human language.

Our proposed formulation for neuron calibration is lightweight and applicable to general feed-forward neural networks-based models.

Sentence scoring aims at measuring the likelihood score of a sentence and is widely usedinnatural language processing scenarios, likereranking, which isto select the best sentence from multiple candidates.

London MathSociety Lecture Note Series.