5 papers
FAIRWELL: Fair Multimodal Self-Supervised Learning for Wellbeing Prediction
Jiaee Cheong, Abtin Mogharabin, Paul Liang +2
Early efforts on leveraging self-supervised learning (SSL) to improve machine learning (ML) fairness has proven promising. However, such an approach has yet to be explored within a…
L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation
Hazal Mogultay Ozcan, Sinan Kalkan, Fatos T. Yarman-Vural
In this paper, we propose a novel model called Learnable VAE (L-VAE), which learns a disentangled representation together with the hyperparameters of the cost function. L-VAE can b…
PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval
Osman Tursun, Sinan Kalkan, Simon Denman +1
Zero-shot Composed Image Retrieval (ZS-CIR) enables image search using a reference image and a text prompt without requiring specialized text-image composition networks trained on…
Machine Learning Fairness for Depression Detection using EEG Data
Angus Man Ho Kwok, Jiaee Cheong, Sinan Kalkan +1
This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using differ…
U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
Jiaee Cheong, Aditya Bangar, Sinan Kalkan +1
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitas…