50 citations · 50 across the 6 of their papers we have counts for
5 papers · 1 filter
Post-training 4-bit quantization of convolution networks for rapid-deployment
Ron Banner, Yury Nahshan, Elad Hoffer +1
Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources. Neural network quantizatio…
Scalable Methods for 8-bit Training of Neural Networks
Ron Banner, Itay Hubara, Elad Hoffer +1
Quantized Neural Networks (QNNs) are often used to improve network efficiency during the inference phase, i.e. after the network has been trained. Extensive research in the field s…
Task Agnostic Continual Learning Using Online Variational Bayes
Chen Zeno, Itay Golan, Elad Hoffer +1
Catastrophic forgetting is the notorious vulnerability of neural networks to the change of the data distribution while learning. This phenomenon has long been considered a major ob…
Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer, Ron Banner, Itay Golan +1
Over the past few years, Batch-Normalization has been commonly used in deep networks, allowing faster training and high performance for a wide variety of applications. However, the…
On the Blindspots of Convolutional Networks
Elad Hoffer, Shai Fine, Daniel Soudry
Deep convolutional network has been the state-of-the-art approach for a wide variety of tasks over the last few years. Its successes have, in many cases, turned it into the default…