Augment your batch: better training with larger batches. - Elad Hoffer; Tal Ben-Nun; Itay Hubara; Niv Giladi; Torsten Hoefler; Daniel Soudry (CoRR, 2019)

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)

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling. - Niv Giladi; Zvika Ben-Haim; Sella Nevo; Yossi Matias; Daniel Soudry (NeurIPS, 2021)

Flood forecasting with machine learning models in an operational framework. - Sella Nevo; Efrat Morin; Adi Gerzi Rosenthal; Asher Metzger; Chen Barshai; Dana Weitzner; Dafi Voloshin; Frederik Kratzert; Gal Elidan; Gideon Dror; Gregory Begelman; Grey Nearing; et al. (CoRR, 2021)

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks. - Dan Levi; Liran Gispan; Niv Giladi; Ethan Fetaya (Sensors, 2022)

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)

Tensor-Parallelism with Partially Synchronized Activations. - Itay Lamprecht; Asaf Karnieli; Yair Hanani; Niv Giladi; Daniel Soudry (CoRR, 2025)