3 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
Yusuf Meric Karadag, Ismail Talaz, Ipek Gursel Dino +1
The problem of Time-series Forecasting is generally addressed by recurrent, Transformer-based and the recently proposed Mamba-based architectures. However, existing architectures g…
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…
Part-based Quantitative Analysis for Heatmaps
Osman Tursun, Sinan Kalkan, Simon Denman +2
Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhanc…
Uncertainty-based Fairness Measures
Selim Kuzucu, Jiaee Cheong, Hatice Gunes +1
Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means f…