zahraatashgahi

ALACPD

Change-point detection using neural networks

CTRE

[Machine Learning Journal (ECML-PKDD 2022 journal track)] A Brain-inspired Algorithm for Training Highly Sparse Neural Networks

CV

lttb-py

Largest-Triangle-Three-Buckets (LTTB) downsampling algorithm in Python

NeuroFS

[TMLR] Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

Neuron-Attribution

[ECAI 2024] Unveiling the Power of Sparse Neural Networks for Feature Selection

PALS

[ECML-PKDD 2024] Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers

PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

QuickSelection

[Machine Learning Journal (ECML-PKDD 2022 journal track)] Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

zahraatashgahi.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

zahraatashgahi/ALACPD

Change-point detection using neural networks

zahraatashgahi/QuickSelection

[Machine Learning Journal (ECML-PKDD 2022 journal track)] Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

zahraatashgahi/NeuroFS

[TMLR] Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

zahraatashgahi/CTRE

[Machine Learning Journal (ECML-PKDD 2022 journal track)] A Brain-inspired Algorithm for Training Highly Sparse Neural Networks

zahraatashgahi/Neuron-Attribution

[ECAI 2024] Unveiling the Power of Sparse Neural Networks for Feature Selection

zahraatashgahi/CV

zahraatashgahi/lttb-py

Largest-Triangle-Three-Buckets (LTTB) downsampling algorithm in Python

zahraatashgahi/PALS

[ECML-PKDD 2024] Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers

zahraatashgahi/PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

zahraatashgahi/zahraatashgahi.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes