Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in …

Abstract Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view
scene synthesis. It addresses the challenges of lengthy training times and slow rendering …

Pruning the parameters of deep neural networks has generated intense interest due to
potential savings in time, memory and energy both during training and at test time. Recent …

With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised
a lot of attention currently on post-training pruning (iterative magnitude pruning), and before …

In this paper, we introduce a new perspective on training deep neural networks capable of
state-of-the-art performance without the need for the expensive over-parameterization by …

Can models with particular structure avoid being biased towards spurious correlation in out-
of-distribution (OOD) generalization? Peters et al.(2016) provides a positive answer for …

Large machine learning models are revolutionary technologies of artificial intelligence
whose bottlenecks include huge computational expenses, power, and time used both in the …

In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …