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20222026
most citedAdvancing Minority Stress Detection with Transformers: Insights from the Social Media Datasets

5 citations · 19 across the 18 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.LG2025

Global Cross-Time Attention Fusion for Enhanced Solar Flare Prediction from Multivariate Time Series

Onur Vural, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Multivariate time series classification is increasingly investigated in space weather research as a means to predict intense solar flare events, which can cause widespread disrupti…

cs.LG2025

TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation

MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Generating high-quality synthetic time series is a fundamental yet challenging task across domains such as forecasting and anomaly detection, where real data can be scarce, noisy,…

cs.CL2025★ 5 cited

Advancing Minority Stress Detection with Transformers: Insights from the Social Media Datasets

Santosh Chapagain, Cory J Cascalheira, Shah Muhammad Hamdi +2

Individuals from sexual and gender minority groups experience disproportionately high rates of poor health outcomes and mental disorders compared to their heterosexual and cisgende…

cs.LG2025★ 2 cited

Pruning Strategies for Backdoor Defense in LLMs

Santosh Chapagain, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Backdoor attacks are a significant threat to the performance and integrity of pre-trained language models. Although such models are routinely fine-tuned for downstream NLP tasks, r…

cs.LG2025

Advancing Hate Speech Detection with Transformers: Insights from the MetaHate

Santosh Chapagain, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Hate speech is a widespread and harmful form of online discourse, encompassing slurs and defamatory posts that can have serious social, psychological, and sometimes physical impact…

cs.LG2025

AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series Generation

MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Data augmentation can significantly enhance the performance of machine learning tasks by addressing data scarcity and improving generalization. However, generating time series data…