| How does batch normalization help binary training? E Sari, M Belbahri, VP Nia arXiv preprint arXiv:1909.09139, 2019 | 40 | 2019 |
| Differentiable mask for pruning convolutional and recurrent networks RK Ramakrishnan, E Sari, VP Nia 2020 17th Conference on Computer and Robot Vision (CRV), 222-229, 2020 | 16 | 2020 |
| Adaptive Binary-Ternary Quantization R Razani, G Morin, E Sari, VP Nia Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 10 | 2021 |
| Foothill: A quasiconvex regularization for edge computing of deep neural networks M Belbahri, E Sari, S Darabi, VP Nia International Conference on Image Analysis and Recognition, 3-14, 2019 | 6 | 2019 |
| Demystifying and Generalizing BinaryConnect T Dockhorn, Y Yu, E Sari, M Zolnouri, V Partovi Nia Advances in Neural Information Processing Systems 34, 2021 | 5 | 2021 |
| Batch Normalization in Quantized Networks E Sari, VP Nia arXiv preprint arXiv:2004.14214, 2020 | 5 | 2020 |
| iRNN: Integer-only Recurrent Neural Network E Sari, V Courville, VP Nia arXiv preprint arXiv:2109.09828, 2021 | 3 | 2021 |
| Smart Ternary Quantization G Morin, R Razani, VP Nia, E Sari arXiv preprint arXiv:1909.12205, 2019 | 3 | 2019 |
| Understanding BatchNorm in Ternary Training E Sari, VP Nia Journal of Computational Vision and Imaging Systems 5 (1), 2-2, 2019 | 2 | 2019 |
| Neural network pruning VP NIA, RK RAMAKRISHNAN, EH SARI US Patent App. 17/012,818, 2021 | 1 | 2021 |
| Foothill: A Quasiconvex Regularization Function M Belbahri, E Sari, S Darabi, VP Nia | | 2019 |
| Foothill Regularizer as a Binary Quantizer M Belbahri, E Sari, S Darabi, X Li, M Courbariaux, VP Nia | | |
Ramchalam Kinattinkara RamakrishnanQualcomm AI Research, TorontoVerified email at mail.mcgill.ca
