41 citations · 43 across the 2 of their papers we have counts for
6 papers
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, Daniel Soudry
Background: Deep learning models are typically trained using stochastic gradient descent or one of its variants. These methods update the weights using their gradient, estimated fr…
No bad local minima: Data independent training error guarantees for multilayer neural networks
Daniel Soudry, Yair Carmon
We use smoothed analysis techniques to provide guarantees on the training loss of Multilayer Neural Networks (MNNs) at differentiable local minima. Specifically, we examine MNNs wi…
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
Matthieu Courbariaux, Itay Hubara, Daniel Soudry +2
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activatio…
Binarized Neural Networks
Itay Hubara, Daniel Soudry, Ran El Yaniv
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at tra…
Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation
Zhiyong Cheng, Daniel Soudry, Zexi Mao +1
Compared to Multilayer Neural Networks with real weights, Binary Multilayer Neural Networks (BMNNs) can be implemented more efficiently on dedicated hardware. BMNNs have been demon…
An exact reduction of the master equation to a strictly stable system with an explicit expression for the stationary distribution
Daniel Soudry, Ron Meir
The evolution of a continuous time Markov process with a finite number of states is usually calculated by the Master equation - a linear differential equations with a singular gene…