12 citations · 43 across the 22 of their papers we have counts for
5 papers · 1 filter
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
Prince Aboagye, Yan Zheng, Junpeng Wang +8
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assesse…
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Prince O Aboagye, Yan Zheng, Michael Yeh +6
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignmen…
How Does Adversarial Fine-Tuning Benefit BERT?
Javid Ebrahimi, Hao Yang, Wei Zhang
Adversarial training (AT) is one of the most reliable methods for defending against adversarial attacks in machine learning. Variants of this method have been used as regularizatio…
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
Archit Rathore, Sunipa Dev, Jeff M. Phillips +6
Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and at…
How Can Self-Attention Networks Recognize Dyck-n Languages?
Javid Ebrahimi, Dhruv Gelda, Wei Zhang
We focus on the recognition of Dyck-n () languages with self-attention (SA) networks, which has been deemed to be a difficult task for these networks. We compare the…