Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Large language models (LLMs) have shown remarkable capabilities in language
understanding and generation. However, such impressive capability typically comes with a …

Structural pruning enables model acceleration by removing structurally-grouped parameters
from neural networks. However, the parameter-grouping patterns vary widely across …

Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-
tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers …

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

In this paper, we study dataset distillation (DD), from a novel perspective and introduce
a\emph {dataset factorization} approach, termed\emph {HaBa}, which is a plug-and-play …

Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …

In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge
Factorization (KF). The core idea of KF lies in the modularization and assemblability of …