Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Abstract Knowledge distillation aims to compress a powerful yet cumbersome teacher model
into a lightweight student model without much sacrifice of performance. For this purpose …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

In this paper, we explore a novel model reusing task tailored for graph neural networks
(GNNs), termed as" deep graph reprogramming". We strive to reprogram a pre-trained GNN …

Recently, model compression has been widely used for the deployment of cumbersome
deep models on resource-limited edge devices in the performance-demanding industrial …

Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and
application areas, efforts are being made to endow these devices with enough intelligence …