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Dimitris Paparas

2 accepted papers

2026

A Robust Optimization Guided Pruning Framework for Vision and Large Language Models

ICML 2026poster

Pruning is a common approach to reduce the memory footprint and inference cost of large vision and language models. As these architectures continue to scale, one-shot pruning methods - i.e. approaches that prune the network without any retraining - have become increasingly attractive. Many popular o…

Cited by 0SourceScholar
2025

Scaling Laws for Downstream Task Performance in Machine Translation

ICLR 2025poster

Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then fi…

Cited by 2SourcePDFScholar