Search Results for author:
Found 8 papers, 3 papers with code
Why Don't Prompt-Based Fairness Metrics Correlate?
CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics.
Fairness-Aware Structured Pruning in Transformers
The increasing size of large language models (LLMs) has introduced challenges in their training and inference.
Should We Attend More or Less? Modulating Attention for Fairness
Specifically, we study the relationship between the entropy of the attention distribution and the model's performance and fairness.
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset.
Fast Strain Estimation and Frame Selection in Ultrasound Elastography using Machine Learning
In the inference stage, we use dynamic programming (DP) to compute an initial displacement estimate of around 1% of the samples, and then decompose this sparse displacement into a linear combination of the 12 displacement modes.
Automatic Frame Selection using CNN in Ultrasound Elastography
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.
Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus.
Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods.
