Soudry, Daniel

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv

Advanced Neural Network ApplicationsAdvanced Memory and Neural Computing

Cited by 2,106

Binarized Neural Networks

Hubara, I., Soudry, D., & Yaniv, R. E. (2016) · arXiv

Advanced Neural Network ApplicationsAdvanced Memory and Neural Computing

Cited by 1,667

Scalable Methods for 8-bit Training of Neural Networks

Banner, R., Hubara, I., Hoffer, E., & Soudry, D. (2018) · arXiv

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 193

Augment your batch: Improving generalization through instance repetition

Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., & Soudry, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 166

Post-training 4-bit quantization of convolution networks for rapid-deployment

Banner, R., Nahshan, Y., Hoffer, E., & Soudry, D. (2018) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 115

Implicit Bias of Gradient Descent on Linear Convolutional Networks

Gunasekar, S., Lee, J., Soudry, D., & Srebro, N. (2018) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 75

Neural gradients are near-lognormal: improved quantized and sparse training

Chmiel, B., Ben-Uri, L., Shkolnik, M., Hoffer, E., Banner, R., & Soudry, D. (2020) · arXiv

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 17