Elad Hoffer
Elad Hoffer
PhD, Research @ Habana Labs
Verified email at habana.ai - Homepage
TitleCited byYear
Deep metric learning using triplet network
E Hoffer, N Ailon
International Workshop on Similarity-Based Pattern Recognition, 84-92, 2015
5292015
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
1992017
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
1382018
Exponentially vanishing sub-optimal local minima in multilayer neural networks
D Soudry, E Hoffer
arXiv preprint arXiv:1702.05777, 2017
492017
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
322018
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
312018
Fix your classifier: the marginal value of training the last weight layer
E Hoffer, I Hubara, D Soudry
arXiv preprint arXiv:1801.04540, 2018
252018
Semi-supervised deep learning by metric embedding
E Hoffer, N Ailon
arXiv preprint arXiv:1611.01449, 2016
182016
Deep unsupervised learning through spatial contrasting
E Hoffer, I Hubara, N Ailon
arXiv preprint arXiv:1610.00243, 2016
142016
Image difference based segmentation using recursive neural networks
DDBD Rubin, E Hoffer
US Patent App. 10/148,872, 2018
92018
ACIQ: Analytical Clipping for Integer Quantization of neural networks
R Banner, Y Nahshan, E Hoffer, D Soudry
arXiv preprint arXiv:1810.05723, 2018
82018
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
82018
Post training 4-bit quantization of convolutional networks for rapid-deployment
R Banner, Y Nahshan, D Soudry
Advances in Neural Information Processing Systems, 7948-7956, 2019
72019
Augment your batch: better training with larger batches
E Hoffer, T Ben-Nun, I Hubara, N Giladi, T Hoefler, D Soudry
arXiv preprint arXiv:1901.09335, 2019
62019
Task Agnostic Continual Learning Using Online Variational Bayes
C Zeno, I Golan, E Hoffer, D Soudry
arXiv preprint arXiv:1803.10123, 2018
52018
Quantized back-propagation: Training binarized neural networks with quantized gradients
I Hubara, E Hoffer, D Soudry
22018
Spatial contrasting for deep unsupervised learning
E Hoffer, I Hubara, N Ailon
arXiv preprint arXiv:1611.06996, 2016
12016
The Knowledge Within: Methods for Data-Free Model Compression
M Haroush, I Hubara, E Hoffer, D Soudry
arXiv preprint arXiv:1912.01274, 2019
2019
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
2019
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
2019
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