Elad Hoffer
Elad Hoffer
PhD, Research @ Habana Labs
Verified email at habana.ai - Homepage
Title
Cited by
Cited by
Year
Deep metric learning using triplet network
E Hoffer, N Ailon
International workshop on similarity-based pattern recognition, 84-92, 2015
11872015
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
E Hoffer, I Hubara, D Soudry
arXiv preprint arXiv:1705.08741, 2017
4682017
The implicit bias of gradient descent on separable data
D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro
The Journal of Machine Learning Research 19 (1), 2822-2878, 2018
3912018
Post-training 4-bit quantization of convolution networks for rapid-deployment
R Banner, Y Nahshan, E Hoffer, D Soudry
arXiv preprint arXiv:1810.05723, 2018
1422018
Scalable methods for 8-bit training of neural networks
R Banner, I Hubara, E Hoffer, D Soudry
arXiv preprint arXiv:1805.11046, 2018
1362018
Norm matters: efficient and accurate normalization schemes in deep networks
E Hoffer, R Banner, I Golan, D Soudry
arXiv preprint arXiv:1803.01814, 2018
1042018
Exponentially vanishing sub-optimal local minima in multilayer neural networks
D Soudry, E Hoffer
arXiv preprint arXiv:1702.05777, 2017
812017
Fix your classifier: the marginal value of training the last weight layer
E Hoffer, I Hubara, D Soudry
arXiv preprint arXiv:1801.04540, 2018
592018
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
57*2020
Bayesian gradient descent: Online variational Bayes learning with increased robustness to catastrophic forgetting and weight pruning
C Zeno, I Golan, E Hoffer, D Soudry
47*2018
The knowledge within: Methods for data-free model compression
M Haroush, I Hubara, E Hoffer, D Soudry
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
342020
Semi-supervised deep learning by metric embedding
E Hoffer, N Ailon
arXiv preprint arXiv:1611.01449, 2016
292016
Aciq: Analytical clipping for integer quantization of neural networks
R Banner, Y Nahshan, E Hoffer, D Soudry
242018
Deep unsupervised learning through spatial contrasting
E Hoffer, I Hubara, N Ailon
arXiv preprint arXiv:1610.00243, 2016
232016
Mix & match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency
E Hoffer, B Weinstein, I Hubara, T Ben-Nun, T Hoefler, D Soudry
arXiv preprint arXiv:1908.08986, 2019
52019
Neural gradients are lognormally distributed: understanding sparse and quantized training
B Chmiel, L Ben-Uri, M Shkolnik, E Hoffer, R Banner, D Soudry
arXiv e-prints, arXiv: 2006.08173, 2020
4*2020
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
N Giladi, MS Nacson, E Hoffer, D Soudry
arXiv preprint arXiv:1909.12340, 2019
42019
Quantized back-propagation: Training binarized neural networks with quantized gradients
I Hubara, E Hoffer, D Soudry
32018
Task Agnostic Continual Learning Using Online Variational Bayes. arXiv e-prints, art
C Zeno, I Golan, E Hoffer, D Soudry
arXiv preprint arXiv:1803.10123, 2018
22018
The Implicit Bias of Gradient Descent on Separable Data. arXiv e-prints, art
D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro
arXiv preprint arXiv:1710.10345, 2017
22017
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