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Tomer Porian

2 accepted papers

2025

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets

NeurIPS 2025poster

In studies of transferable learning, scaling laws are obtained for various important foundation models to predict their properties and performance at larger scales. Taking language-vision learning as example, we show here how scaling law derivation can also be used for model and dataset comparison,…

Cited by 0SourcecodeScholar
2024

Resolving Discrepancies in Compute-Optimal Scaling of Language Models

NeurIPS 2024spotlight

Kaplan et al. and Hoffmann et al. developed influential scaling laws for the optimal model size as a function of the compute budget, but these laws yield substantially different predictions. We explain the discrepancy by reproducing the Kaplan scaling law on two datasets (OpenWebText2 and RefinedWeb…

Cited by 13SourcePDFScholar