Motivated by the efficiency and rapid convergence of pre-trained models ...
Neural Implicit Representations (NIR) have gained significant attention
...
Token-based masked generative models are gaining popularity for their fa...
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states t...
Self-supervised Video Representation Learning (VRL) aims to learn
transf...
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothes...
Neural network quantization aims to transform high-precision weights and...
In real-world scenarios, subgraphs of a larger global graph may be
distr...
In practical federated learning scenarios, the participating devices may...
Continual learning (CL) aims to learn a sequence of tasks without forget...
A dataset is a shred of crucial evidence to describe a task. However, ea...
As deep neural networks are growing in size and being increasingly deplo...
While existing federated learning approaches mostly require that clients...
There has been a surge of interest in continual learning and federated
l...
The order of the tasks a continual learning model encounters may have la...
While variational dropout approaches have been shown to be effective for...