The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

As their size increases, Large Languages Models (LLMs) are natural candidates for network
pruning methods: approaches that drop a subset of network weights while striving to …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Many applications require sparse neural networks due to space or inference time
restrictions. There is a large body of work on training dense networks to yield sparse …

Abstract Invariant Risk Minimization (IRM) is an emerging invariant feature extracting
technique to help generalization with distributional shift. However, we find that there exists a …

Since hardware resources are limited, the objective of training deep learning models is
typically to maximize accuracy subject to the time and memory constraints of training and …

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 …

Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise
sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between …