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Found 5 papers, 2 papers with code
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models
Compression techniques have been crucial in advancing machine learning by enabling efficient training and deployment of large-scale language models.
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.
Vision Transformers for Mobile Applications: A Short Survey
Vision Transformers (ViTs) have demonstrated state-of-the-art performance on many Computer Vision Tasks.
Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages
In this work, we explore the applicability of low-compute approaches such as language adapters in the context of this low-resource double-bind.
Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language
Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study.
