4 citations · 13 across the 10 of their papers we have counts for
7 papers · 1 filter
Using Self-supervised Learning Can Improve Model Fairness
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides +3
Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and la…
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024
Dimitris Spathis, Aaqib Saeed, Ali Etemad +6
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.
Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement
Aaqib Saeed, Dimitris Spathis, Jungwoo Oh +2
Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is…
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
Chi Ian Tang, Lorena Qendro, Dimitris Spathis +3
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature…
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides +3
Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and lab…
The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models
Dimitris Spathis, Fahim Kawsar
Large Language Models (LLMs) have demonstrated remarkable generalization across diverse tasks, leading individuals to increasingly use them as personal assistants and universal com…