Elad Hoffer

According to our database, Elad Hoffer authored at least 28 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

DropCompute: simple and more robust distributed synchronous training via compute variance reduction.

Niv Giladi, Shahar Gottlieb, Moran Shkolnik, Asaf Karnieli, Ron Banner, Elad Hoffer, Kfir Y. Levy, Daniel Soudry

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats.

Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Energy awareness in low precision neural networks.

Nurit Spingarn-Eliezer, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Tomer Michaeli

CoRR, 2022

Power Awareness in Low Precision Neural Networks.

Nurit Spingarn-Eliezer, Ron Banner, Hilla Ben-Yaacov, Elad Hoffer, Tomer Michaeli

Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

2021

Task-Agnostic Continual Learning Using Online Variational Bayes With Fixed-Point Updates.

Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry

Neural Comput., 2021

Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning.

Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry

CoRR, 2021

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

Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Neural gradients are lognormally distributed: understanding sparse and quantized training.

Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry

CoRR, 2020

At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?

Niv Giladi, Mor Shpigel Nacson, Elad Hoffer, Daniel Soudry

Proceedings of the 8th International Conference on Learning Representations, 2020

Augment Your Batch: Improving Generalization Through Instance Repetition.

Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

The Knowledge Within: Methods for Data-Free Model Compression.

Matan Haroush, Itay Hubara, Elad Hoffer, Daniel Soudry

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency.

Elad Hoffer, Berry Weinstein, Itay Hubara, Tal Ben-Nun, Torsten Hoefler, Daniel Soudry

CoRR, 2019

Augment your batch: better training with larger batches.

Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry

CoRR, 2019

2018

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro

J. Mach. Learn. Res., 2018

ACIQ: Analytical Clipping for Integer Quantization of neural networks.

Ron Banner, Yury Nahshan, Elad Hoffer, Daniel Soudry

CoRR, 2018

Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning.

Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry

CoRR, 2018

On the Blindspots of Convolutional Networks.

Elad Hoffer, Shai Fine, Daniel Soudry

CoRR, 2018

Norm matters: efficient and accurate normalization schemes in deep networks.

Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable methods for 8-bit training of neural networks.

Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro

Proceedings of the 6th International Conference on Learning Representations, 2018

Exponentially vanishing sub-optimal local minima in multilayer neural networks.

Daniel Soudry, Elad Hoffer

Proceedings of the 6th International Conference on Learning Representations, 2018

Fix your classifier: the marginal value of training the last weight layer.

Elad Hoffer, Itay Hubara, Daniel Soudry

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

The Implicit Bias of Gradient Descent on Separable Data.

Daniel Soudry, Elad Hoffer, Nathan Srebro

CoRR, 2017

Train longer, generalize better: closing the generalization gap in large batch training of neural networks.

Elad Hoffer, Itay Hubara, Daniel Soudry

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Semi-supervised deep learning by metric embedding.

Elad Hoffer, Nir Ailon

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Spatial contrasting for deep unsupervised learning.

Elad Hoffer, Itay Hubara, Nir Ailon

CoRR, 2016

Deep unsupervised learning through spatial contrasting.

Elad Hoffer, Itay Hubara, Nir Ailon

CoRR, 2016

2015

Deep metric learning using Triplet network.

Elad Hoffer, Nir Ailon

Proceedings of the 3rd International Conference on Learning Representations, 2015