Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application.
In this work, we focus on developing universal user representation model.
Given the recent geometrical classification of 6d $(1, 0)$ SCFTs, a major question is how to compute for this large class their elliptic genera.
High Energy Physics - Theory Mathematical Physics Mathematical Physics
A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.
Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features.
Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.
With the statistical procedure, the proposed method is universal and fast; moreover, it is robust against traditional EED challenges (such as error accumulations, spurious correlations, and even bad data in core area).