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Elad Hoffer

Graduate student at Technion, Deep Learning Researcher

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Welcome to my page

I’m Elad Hoffer, PhD candidate at Technion. My research is focused on Deep Learning of representations, and I’m also interested in related topics of machine learning and computer vision.

My CV is available here


Elad Hoffer, Itay Hubara, Daniel Soudry - Fix your classifier: the marginal value of training the last weight layer - ICLR 2018 [ArXiv]

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro - The Implicit Bias of Gradient Descent on Separable Data - ICLR 2018 [ArXiv]

Daniel Soudry, Elad Hoffer - Exponentially vanishing sub-optimal local minima in multilayer neural networks - ICLR 2018 - workshop [ArXiv]

Elad Hoffer, Itay Hubara, Daniel Soudry - Train longer, generalize better: closing the generalization gap in large batch training of neural networks - NIPS 2017 Oral presentation (1.2% acceptance rate) [ArXiv][Code][Poster][Presentation][Video]

Elad Hoffer, Nir Ailon - Semi-supervised deep learning by metric embedding - ICLR 2017 - workshop [ArXiv][Code]

Elad Hoffer, Itay Hubara, Nir Ailon - Spatial contrasting for deep unsupervised learning - NIPS 2016 - Workshop on Interpretable Machine Learning in Complex Systems [ArXiv][Code]

Elad Hoffer, Nir Ailon - Deep metric learning using Triplet network - ICLR 2015 [ArXiv][Poster][Code]

Additional works

Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry - Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning [ArXiv]

Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry - Norm matters: efficient and accurate normalization schemes in deep networks [ArXiv]

Elad Hoffer, Shai Fine, Daniel Soudry - On the Blindspots of Convolutional Networks [ArXiv]

Elad Hoffer, Itay Hubara, Nir Ailon - Deep unsupervised learning through spatial contrasting [ArXiv][Code]

Sparse Deep Learning - Merging double sparsity with Deep NN [Report] [Presentation]


I gave a course about Deep Learning at Technion:

Slides and tutorials are available here

Videos are available on YouTube (Thanks to Michael Zibulevsky):


Some talks I’ve given on my research and related topics:


You can contact me at my email: