Search Results for author: Zahra Atashgahi

Found 7 papers, 7 papers with code

Where to Pay Attention in Sparse Training for Feature Selection?

1 code implementation26 Nov 2022 Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu

Our proposed approach outperforms the state-of-the-art methods in terms of selecting informative features while reducing training iterations and computational costs substantially.

Memory-free Online Change-point Detection: A Novel Neural Network Approach

1 code implementation8 Jul 2022 Zahra Atashgahi, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy

We show that ALACPD, on average, ranks first among state-of-the-art CPD algorithms in terms of quality of the time series segmentation, and it is on par with the best performer in terms of the accuracy of the estimated change-points.

Change Point Detection Time Series +1

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration

1 code implementation NeurIPS 2021 Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

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-training pruning (pruning at initialization).

Network Pruning Sparse Learning

Topological Insights into Sparse Neural Networks

3 code implementations24 Jun 2020 Shiwei Liu, Tim Van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu

However, comparing different sparse topologies and determining how sparse topologies evolve during training, especially for the situation in which the sparse structure optimization is involved, remain as challenging open questions.

A Brain-inspired Algorithm for Training Highly Sparse Neural Networks

2 code implementations17 Mar 2019 Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy

Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, Cosine similarity-based and Random Topology Exploration (CTRE), evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward.

Learning Theory

Cannot find the paper you are looking for? You can Submit a new open access paper.