On the selection of initialization and activation function for deep neural networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1805.08266, 2018 | 47 | 2018 |
On the impact of the activation function on deep neural networks training S Hayou, A Doucet, J Rousseau International Conference on Machine Learning, 2672-2680, 2019 | 44 | 2019 |
On the impact of the activation function on deep neural networks training S Hayou, A Doucet, J Rousseau 36th International Conference on Machine Learning (ICML 2019), 2019 | 44 | 2019 |
Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1905.13654, 2019 | 6 | 2019 |
Mean-field behaviour of neural tangent kernel for deep neural networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1905.13654, 2019 | 6 | 2019 |
Training dynamics of deep networks using stochastic gradient descent via neural tangent kernel S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1905.13654, 2019 | 6 | 2019 |
Pruning untrained neural networks: Principles and analysis S Hayou, JF Ton, A Doucet, YW Teh 9th International Conference on Learning Representations (ICLR 2021), 2020 | 4 | 2020 |
On the selection of initialization and activation function for deep neural networks. arXiv Prepr S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1805.08266, 2018 | 2 | 2018 |
Robust Pruning at Initialization S Hayou, JF Ton, A Doucet, YW Teh | 1 | 2021 |
Stable ResNet S Hayou, E Clerico, B He, G Deligiannidis, A Doucet, J Rousseau 24th International Conference on Artificial Intelligence and Statistics …, 2020 | | 2020 |
On the overestimation of the largest eigenvalue of a covariance matrix S Hayou arXiv preprint arXiv:1708.03551, 2017 | | 2017 |
Cleaning the correlation matrix with a denoising autoencoder S Hayou arXiv preprint arXiv:1708.02985, 2017 | | 2017 |
On the Impact of the Activation Function on Deep Neural Networks Training: Supplementary material S Hayou, A Doucet, J Rousseau | | |