Binarized neural networks I Hubara, M Courbariaux, D Soudry, R El-Yaniv, Y Bengio Advances in neural information processing systems 29, 2016 | 5710* | 2016 |
Quantized neural networks: Training neural networks with low precision weights and activations I Hubara, M Courbariaux, D Soudry, R El-Yaniv, Y Bengio Journal of Machine Learning Research 18 (187), 1-30, 2018 | 2169 | 2018 |
Simultaneous denoising, deconvolution, and demixing of calcium imaging data EA Pnevmatikakis, D Soudry, Y Gao, TA Machado, J Merel, D Pfau, ... Neuron 89 (2), 285-299, 2016 | 1052 | 2016 |
Train longer, generalize better: closing the generalization gap in large batch training of neural networks E Hoffer, I Hubara, D Soudry Advances in neural information processing systems 30, 2017 | 944 | 2017 |
The implicit bias of gradient descent on separable data D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro Journal of Machine Learning Research 19 (70), 1-57, 2018 | 926 | 2018 |
Post training 4-bit quantization of convolutional networks for rapid-deployment R Banner, Y Nahshan, D Soudry Advances in Neural Information Processing Systems 32, 2019 | 665* | 2019 |
Implicit bias of gradient descent on linear convolutional networks S Gunasekar, JD Lee, D Soudry, N Srebro Advances in neural information processing systems 31, 2018 | 430 | 2018 |
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018 | 423 | 2018 |
Scalable methods for 8-bit training of neural networks R Banner, I Hubara, E Hoffer, D Soudry Advances in neural information processing systems 31, 2018 | 373 | 2018 |
Memristor-based multilayer neural networks with online gradient descent training D Soudry, D Di Castro, A Gal, A Kolodny, S Kvatinsky IEEE transactions on neural networks and learning systems 26 (10), 2408-2421, 2015 | 305 | 2015 |
Kernel and rich regimes in overparametrized models B Woodworth, S Gunasekar, JD Lee, E Moroshko, P Savarese, I Golan, ... Conference on Learning Theory, 3635-3673, 2020 | 302 | 2020 |
Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights D Soudry, I Hubara, R Meir Advances in neural information processing systems 27, 2014 | 298 | 2014 |
Augment your batch: Improving generalization through instance repetition E Hoffer, T Ben-Nun, I Hubara, N Giladi, T Hoefler, D Soudry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 296* | 2020 |
No bad local minima: Data independent training error guarantees for multilayer neural networks D Soudry, Y Carmon arXiv preprint arXiv:1605.08361, 2016 | 259 | 2016 |
Accurate post training quantization with small calibration sets I Hubara, Y Nahshan, Y Hanani, R Banner, D Soudry International Conference on Machine Learning, 4466-4475, 2021 | 232* | 2021 |
Norm matters: efficient and accurate normalization schemes in deep networks E Hoffer, R Banner, I Golan, D Soudry Advances in Neural Information Processing Systems 31, 2018 | 180 | 2018 |
How do infinite width bounded norm networks look in function space? P Savarese, I Evron, D Soudry, N Srebro Conference on Learning Theory, 2667-2690, 2019 | 155 | 2019 |
Convergence of gradient descent on separable data MS Nacson, J Lee, S Gunasekar, PHP Savarese, N Srebro, D Soudry arXiv preprint arXiv:1803.01905, 2018 | 154 | 2018 |
Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis Y Dordek, D Soudry, R Meir, D Derdikman Elife 5, e10094, 2016 | 154 | 2016 |
A function space view of bounded norm infinite width relu nets: The multivariate case G Ongie, R Willett, D Soudry, N Srebro arXiv preprint arXiv:1910.01635, 2019 | 145 | 2019 |