Deep metric learning using Triplet network. - Elad Hoffer; Nir Ailon (ICLR (Workshop), 2015)

Deep unsupervised learning through spatial contrasting. - Elad Hoffer; Itay Hubara; Nir Ailon (CoRR, 2016)

Spatial contrasting for deep unsupervised learning. - Elad Hoffer; Itay Hubara; Nir Ailon (CoRR, 2016)

Semi-supervised deep learning by metric embedding. - Elad Hoffer; Nir Ailon (ICLR (Workshop), 2017)

Train longer, generalize better: closing the generalization gap in large batch training of neural networks. - Elad Hoffer; Itay Hubara; Daniel Soudry (NIPS, 2017)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Nathan Srebro (CoRR, 2017)

Fix your classifier: the marginal value of training the last weight layer. - Elad Hoffer; Itay Hubara; Daniel Soudry (ICLR (Poster), 2018)

Exponentially vanishing sub-optimal local minima in multilayer neural networks. - Daniel Soudry; Elad Hoffer (ICLR (Workshop), 2018)

The Implicit Bias of Gradient Descent on Separable Data. - Daniel Soudry; Elad Hoffer; Mor Shpigel Nacson; Nathan Srebro (ICLR (Poster), 2018)

Scalable methods for 8-bit training of neural networks. - Ron Banner; Itay Hubara; Elad Hoffer; Daniel Soudry (NeurIPS, 2018)

Norm matters: efficient and accurate normalization schemes in deep networks. - Elad Hoffer; Ron Banner; Itay Golan; Daniel Soudry (NeurIPS, 2018)

On the Blindspots of Convolutional Networks. - Elad Hoffer; Shai Fine; 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)

ACIQ: Analytical Clipping for Integer Quantization of neural networks. - Ron Banner; Yury Nahshan; Elad Hoffer; Daniel Soudry (CoRR, 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)

Augment your batch: better training with larger batches. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CoRR, 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)

The Knowledge Within: Methods for Data-Free Model Compression. - Matan Haroush; Itay Hubara; Elad Hoffer; Daniel Soudry (CVPR, 2020)

Augment Your Batch: Improving Generalization Through Instance Repetition. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CVPR, 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 (ICLR, 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)

Neural gradients are near-lognormal: improved quantized and sparse training. - Brian Chmiel; Liad Ben-Uri; Moran Shkolnik; Elad Hoffer; Ron Banner; Daniel Soudry (ICLR, 2021)

Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. - Brian Chmiel; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Daniel Soudry (CoRR, 2021)

Task-Agnostic Continual Learning Using Online Variational Bayes With Fixed-Point Updates. - Chen Zeno; Itay Golan; Elad Hoffer; Daniel Soudry (Neural Comput., 2021)

Power Awareness in Low Precision Neural Networks. - Nurit Spingarn-Eliezer; Ron Banner; Hilla Ben-Yaacov; Elad Hoffer; Tomer Michaeli (ECCV Workshops (7), 2022)

Energy awareness in low precision neural networks. - Nurit Spingarn-Eliezer; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Tomer Michaeli (CoRR, 2022)

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. - Brian Chmiel; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Daniel Soudry (ICLR, 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 (NeurIPS, 2023)

AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation. - Itay Nakash; Nitay Calderon; Eyal Ben-David; Elad Hoffer; Roi Reichart (CoRR, 2025)