27 citations · 33 across the 7 of their papers we have counts for
10 papers
EnsAug: Augmentation-Driven Ensembles for Human Motion Sequence Analysis
Bikram De, Habib Irani, Vangelis Metsis
Data augmentation is a crucial technique for training robust deep learning models for human motion, where annotated datasets are often scarce. However, generic augmentation methods…
WaveFormer: Wavelet Embedding Transformer for Biomedical Signals
Habib Irani, Bikram De, Vangelis Metsis
Biomedical signal classification presents unique challenges due to long sequences, complex temporal dynamics, and multi-scale frequency patterns that are poorly captured by standar…
DyWPE: Signal-Aware Dynamic Wavelet Positional Encoding for Time Series Transformers
Habib Irani, Vangelis Metsis
Existing positional encoding methods in transformers are fundamentally signal-agnostic, deriving positional information solely from sequence indices while ignoring the underlying s…
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
Arshia Kermani, Veronica Perez-Rosas, Vangelis Metsis
This study presents a systematic comparison of three approaches for the analysis of mental health text using large language models (LLMs): prompt engineering, retrieval augmented g…
Positional Encoding in Transformer-Based Time Series Models: A Survey
Habib Irani, Vangelis Metsis
Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting, anomaly detection, and classif…
Time Series Embedding Methods for Classification Tasks: A Review
Habib Irani, Yasamin Ghahremani, Arshia Kermani +1
Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. Due to their multidimensional nature, time series often n…
BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis
Xiaomin Li, Mykhailo Sakevych, Gentry Atkinson +1
Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interferen…
TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation
Xiaomin Li, Anne Hee Hiong Ngu, Vangelis Metsis
Signal measurement appearing in the form of time series is one of the most common types of data used in medical machine learning applications. Such datasets are often small in size…
TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network
Xiaomin Li, Vangelis Metsis, Huangyingrui Wang +1
Signal measurements appearing in the form of time series are one of the most common types of data used in medical machine learning applications. However, such datasets are often sm…
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
Cody Blakeney, Gentry Atkinson, Nathaniel Huish +3
Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discriminatio…