Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …

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 …

Channel pruning is one of the predominant approaches for deep model compression.
Existing pruning methods either train from scratch with sparsity constraints on channels, or …

The deployment of deep convolutional neural networks (CNNs) in many real world
applications is largely hindered by their high computational cost. In this paper, we propose a …

We propose a practical method for $ L_0 $ norm regularization for neural networks: pruning
the network during training by encouraging weights to become exactly zero. Such …

Given the increasing promise of graph neural networks (GNNs) in real-world applications,
several methods have been developed for explaining their predictions. Existing methods for …

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients'
data privacy and security issues. Private inference (PI) techniques using cryptographic …

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 …