| Survey of dropout methods for deep neural networks A Labach, H Salehinejad, S Valaee arXiv preprint arXiv:1904.13310, 2019 | 217 | 2019 |
| Survey of dropout methods for deep neural networks. arXiv 2019 A Labach, H Salehinejad, S Valaee arXiv preprint arXiv:1904.13310, 1904 | 17 | 1904 |
| A framework for neural network pruning using Gibbs distributions A Labach, S Valaee GLOBECOM 2020-2020 IEEE Global Communications Conference, 1-6, 2020 | 7 | 2020 |
| Survey of dropout methods for deep neural networks (2019) A Labach, H Salehinejad, S Valaee arXiv preprint arXiv:1904.13310, 1904 | 6 | 1904 |
| Duett: Dual event time transformer for electronic health records A Labach, A Pokhrel, XS Huang, S Zuberi, SE Yi, M Volkovs, T Poutanen, ... Machine Learning for Healthcare Conference, 403-422, 2023 | 3 | 2023 |
| MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting L Du, J Xin, A Labach, S Zuberi, M Volkovs, RG Krishnan arXiv preprint arXiv:2311.18780, 2023 | | 2023 |
| Effective Self-Supervised Transformers For Sparse Time Series Data A Labach, A Pokhrel, SE Yi, S Zuberi, M Volkovs, RG Krishnan | | 2022 |
| Regularizing Neural Networks by Stochastically Training Layer Ensembles A Labach, S Valaee 2020 IEEE 30th International Workshop on Machine Learning for Signal …, 2020 | | 2020 |
| 13 Neural network sparsification using Gibbs measures A Labach, S Valaee Edge Intelligence Workshop 711 (23), 10, 2020 | | 2020 |
| Gibbs Pruning: A Framework for Structured and Unstructured Neural Network Pruning AJ Labach University of Toronto (Canada), 2020 | | 2020 |
Shahrokh ValaeeUniversity of TorontoBestätigte E-Mail-Adresse bei ece.utoronto.ca
