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Publicaties
Recent
Atashgahi, Z.
, Mocanu, D. C.
, Veldhuis, R. N. J., & Pechenizkiy, M. (2022).
Memory-free Online Change-point Detection: A Novel Neural Network Approach. ArXiv.org.
https://arxiv.org/abs/2207.03932
Sokar, G. A. Z. N.
, Atashgahi, Z., Pechenizkiy, M.
, & Mocanu, D. C. (2022).
Where to Pay Attention in Sparse Training for Feature Selection? ArXiv.org.
https://doi.org/10.48550/arXiv.2211.14627
Atashgahi, Z., Pieterse, J., Liu, S.
, Mocanu, D. C.
, Veldhuis, R. N. J., & Pechenizkiy, M. (2022).
A brain-inspired algorithm for training highly sparse neural networks.
Machine Learning,
111(12), 4411-4452.
https://doi.org/10.1007/s10994-022-06266-w
Atashgahi, Z., Sokar, G., van der Lee, T.
, Mocanu, E.
, Mocanu, D. C.
, Veldhuis, R., & Pechenizkiy, M. (2022).
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders.
Machine Learning,
111, 377–414.
https://doi.org/10.1007/s10994-021-06063-x
Liu, S., Chen, T.
, Atashgahi, Z., Chen, X., Sokar, G.
, Mocanu, E., Pechenizkiy, M., Wang, Z.
, & Mocanu, D. C. (2022).
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. In
The Tenth International Conference on Learning Representations, ICLR 2022 OpenReview.
https://openreview.net/forum?id=RLtqs6pzj1-¬eId=d7CKVDyMGZi
Liu, S., Chen, T.
, Atashgahi, Z., Chen, X., Sokar, G. A. Z. N.
, Mocanu, E., Pechenizkiy, M., Wang, Z.
, & Mocanu, D. C. (2021).
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2021, Online.
Liu, S., Chen, T., Chen, X.
, Atashgahi, Z., Yin, L., Kou, H., Shen, L., Pechenizkiy, M., Wang, Z.
, & Mocanu, D. C. (2021).
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration (Poster). Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2021, Online.
Kichler, N.
, Atashgahi, Z.
, & Mocanu, D. C. (2021).
Robustness of sparse MLPs for supervised feature selection (poster). Poster session presented at Sparsity in Neural Networks: Advancing Understanding and Practice 2021, Online.
Atashgahi, Z., Sokar, G. A. Z. N., van der Lee, T.
, Mocanu, E.
, Mocanu, D. C.
, Veldhuis, R. N. J., & Pechenizkiy, M. (2021).
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (Extended Abstract). In
BNAIC/BENELEARN 2021: The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning
Atashgahi, Z.
, Mocanu, D. C.
, Veldhuis, R. N. J., & Pechenizkiy, M. (2021).
Unsupervised Online Memory-free Change-point Detection using an Ensemble of LSTM-Autoencoder-based Neural Networks (Extended Abstract). Paper presented at 8th ACM Celebration of Women in Computing womENcourage, Prague, Czech Republic.
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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
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Hallenweg 19
7522NH Enschede
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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
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7500 AE Enschede
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