Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, ie,
model performance deterioration on past tasks when learning a new task. However, the …

Channel pruning has been broadly recognized as an effective technique to reduce the
computation and memory cost of deep convolutional neural networks. However …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

Federated Learning (FL) is a distributed learning paradigm that can learn a global or
personalized model from decentralized datasets on edge devices. However, in the computer …

The ever-increasing large language models (LLMs), though opening a potential path for the
upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards …

The receptive field (RF), which determines the region of time series to be “seen” and used, is
critical to improve the performance for time series classification (TSC). However, the …

Random masks define surprisingly effective sparse neural network models, as has been
shown empirically. The resulting sparse networks can often compete with dense …

As real-world graphs expand in size, larger GNN models with billions of parameters are
deployed. High parameter count in such models makes training and inference on graphs …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …