2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CL2023
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding
Venkata Prabhakara Sarath Nookala, Gaurav Verma, Subhabrata Mukherjee +1
State-of-the-art few-shot learning (FSL) methods leverage prompt-based fine-tuning to obtain remarkable results for natural language understanding (NLU) tasks. While much of the pr…
cs.LG2023★ 2 cited
Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
Amey Agrawal, Sameer Reddy, Satwik Bhattamishra +4
With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rise…