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
7482015
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
E Hoffer, I Hubara, D Soudry
Advances in Neural Information Processing Systems, 1731-1741, 2017
2862017
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
2252018
Scalable methods for 8-bit training of neural networks
R Banner, I Hubara, E Hoffer, D Soudry
Advances in neural information processing systems, 5145-5153, 2018
652018
Norm matters: efficient and accurate normalization schemes in deep networks
E Hoffer, R Banner, I Golan, D Soudry
Advances in Neural Information Processing Systems, 2160-2170, 2018
612018
Exponentially vanishing sub-optimal local minima in multilayer neural networks
D Soudry, E Hoffer
arXiv preprint arXiv:1702.05777, 2017
582017
Fix your classifier: the marginal value of training the last weight layer
E Hoffer, I Hubara, D Soudry
arXiv preprint arXiv:1801.04540, 2018
332018
Semi-supervised deep learning by metric embedding
E Hoffer, N Ailon
arXiv preprint arXiv:1611.01449, 2016
222016
Deep unsupervised learning through spatial contrasting
E Hoffer, I Hubara, N Ailon
arXiv preprint arXiv:1610.00243, 2016
192016
Bayesian gradient descent: Online variational Bayes learning with increased robustness to catastrophic forgetting and weight pruning
C Zeno, I Golan, E Hoffer, D Soudry
arXiv preprint arXiv:1803.10123, 2018
17*2018
ACIQ: analytical clipping for integer quantization of neural networks
R Banner, Y Nahshan, E Hoffer, D Soudry
112018
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
10*2020
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
42020
Quantized back-propagation: Training binarized neural networks with quantized gradients
I Hubara, E Hoffer, D Soudry
22018
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
12019
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
12019
On the Blindspots of Convolutional Networks
E Hoffer, S Fine, D Soudry
arXiv preprint arXiv:1802.05187, 2018
12018
Neural gradients are lognormally distributed: understanding sparse and quantized training
B Chmiel, L Ben-Uri, M Shkolnik, E Hoffer, R Banner, D Soudry
arXiv preprint arXiv:2006.08173, 2020
2020
Image difference based segmentation using recursive neural networks
DDBD Rubin, E Hoffer
US Patent 10,148,872, 2018
2018
Infer2Train: leveraging inference for better training of deep networks
E Hoffer, B Weinstein, I Hubara, S Gofman, D Soudry
The system can't perform the operation now. Try again later.
Articles 1–20