Neurodevelopmental Disorders (NDD) are a group of conditions with onset in the
developmental period characterized by deficits in the cognitive and social areas …

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question
answering. To aggregate clues from scattered texts across multiple paragraphs, a …

The design of more complex and powerful neural network models has significantly
advanced the state-of-the-art in visual object tracking. These advances can be attributed to …

Learning-based image inpainting methods have made remarkable progress in recent years.
Nevertheless, these methods still suffer from issues such as blurring, artifacts, and …

In this paper, we introduce a two-level attention schema, Poolingformer, for long document
modeling. Its first level uses a smaller sliding window pattern to aggregate information from …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …

Pooling is commonly used to improve the computation-accuracy trade-off of convolutional
networks. By aggregating neighboring feature values on the image grid, pooling layers …

Transformer models have achieved superior performance in various natural language
processing tasks. However, the quadratic computational cost of the attention mechanism …

The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence
and has successfully boosted the performance of different models. However, current …

Transformers have become the state-of-the-art architectures for various tasks in Natural
Language Processing (NLP) and Computer Vision (CV); however, their space and …