3 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
FairSSL: Fair Multimodal Self-Supervised Learning
Jiaee Cheong, Abtin Mogharabin, Paul Liang +2
Prevalent multimodal self-supervised learning (SSL) methods rely on the redundancy assumption: that different views share substantial task-relevant information. We argue that this…
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…
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…