Search Results for author:
Found 5 papers, 2 papers with code
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
Given the generational gap in available hardware between lay practitioners and the most endowed institutions, LLMs are becoming increasingly inaccessible as they grow in size.
Multitask Learning Can Improve Worst-Group Outcomes
We primarily consider the standard setting of fine-tuning a pre-trained model, where, following recent work \citep{gururangan2020don, dery2023aang}, we multitask the end task with the pre-training objective constructed from the end task data itself.
Transfer Learning for Structured Pruning under Limited Task Data
We propose a framework which combines structured pruning with transfer learning to reduce the need for task-specific data.
DeMuX: Data-efficient Multilingual Learning
In this paper, we introduce DEMUX, a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set.
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos
In this work, we propose to tackle this new task with a weakly-supervised framework for reference-aware visual grounding in instructional videos, where only the temporal alignment between the transcription and the video segment are available for supervision.
