1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Dynamic Stashing Quantization for Efficient Transformer Training
Guo Yang, Daniel Lo, Robert Mullins +1
Large Language Models (LLMs) have demonstrated impressive performance on a range of Natural Language Processing (NLP) tasks. Unfortunately, the immense amount of computations and m…
cs.LG2022★ 1 cited
Efficient Adversarial Training With Data Pruning
Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov +2
Neural networks are susceptible to adversarial examples-small input perturbations that cause models to fail. Adversarial training is one of the solutions that stops adversarial exa…