2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 2 cited
Targeted Data Generation: Finding and Fixing Model Weaknesses
Zexue He, Marco Tulio Ribeiro, Fereshte Khani
Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Addi…
cs.LG2023★ 1 cited
Collaborative Development of NLP models
Fereshte Khani, Marco Tulio Ribeiro
Despite substantial advancements, Natural Language Processing (NLP) models often require post-training adjustments to enforce business rules, rectify undesired behavior, and align…
cs.LG2022
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Saeid Asgari Taghanaki, Ali Gholami, Fereshte Khani +4
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy. However, we demonstrate that BN co…