Dataset pruning aims to construct a coreset capable of achieving performance comparable
to the original full dataset. Most existing dataset pruning methods rely on snapshot-based …

Sparse neural networks are a key factor in developing resource-efficient machine learning
applications. We propose the novel and powerful sparse learning method Adaptive …

We introduce a meta-learning algorithm for adversarially robust classification. The proposed
method tries to be as model agnostic as possible and optimizes a dataset prior to its …

Distilling knowledge from a large teacher model to a lightweight one is a widely successful
approach for generating compact, powerful models in the semi-supervised learning setting …

Neural networks typically exhibit permutation symmetry, as reordering neurons in each layer
does not change the underlying function they compute. These symmetries contribute to the …

Machine learning (ML) models are overparameterized to support generality and avoid
overfitting. Prior works have shown that these additional parameters can be used for both …

Abstract Model pruning methods reduce memory requirements and inference time of large-
scale pre-trained language models after deployment. However, the actual pruning …

Pruning is a standard technique for reducing the computational cost of deep networks. Many
advances in pruning leverage concepts from the Lottery Ticket Hypothesis (LTH). LTH …

It is commonly observed that deep networks trained for classification exhibit class-selective
neurons in their early and intermediate layers. Intriguingly, recent studies have shown that …

Having large amounts of annotated data significantly impacts the effectiveness of deep
neural networks. However, the annotation task can be very expensive in some domains …