Transformers have achieved great success in many artificial intelligence fields, such as
natural language processing, computer vision, and audio processing. Therefore, it is natural …

A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …

We show that standard Transformers without graph-specific modifications can lead to
promising results in graph learning both in theory and practice. Given a graph, we simply …

Despite the dominance and effectiveness of scaling, resulting in large networks with
hundreds of billions of parameters, the necessity to train overparameterized models remains …

Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision
tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting their …

We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed
$\mathtt {U\text {-} Hop} $, with enhanced memory capacity. Our key contribution is a …

Taxi demand prediction plays a significant role in assisting the pre-allocation of taxi
resources to avoid mismatches between demand and service, particularly in the era of the …

The multiscale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is
a challenging task, due to the feature vanishing as network deepens. To address this …

In-context learning (ICL) of large language models has proven to be a surprisingly effective
method of learning a new task from only a few demonstrative examples. In this paper, we …