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Gabriel Afriat

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
2026

Seeing What’s Wrong: A Trajectory-Guided Approach to Caption Error Detection

ICLR 2026poster

Error detection is critical for enhancing multimodal dataset reliability and downstream model performance. Existing error filters, while increasingly powerful, typically rely on a single similarity score per image–caption pair. This is limiting: captions with subtle errors (e.g., mislabeled objects,…

Cited by 0SourcecodeScholar