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20222026
most citedDebiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey

44 citations · 50 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

Latent Fact-Checking: Detecting Misinformation through Activation Engineering

Pedro T. Barcelos, Otávio Parraga, Marcelo M. Delucis +3

The proliferation of misinformation online has driven demand for scalable detection systems. While most existing approaches rely on surface-level linguistic features or external kn…

cs.LG2026

Continual Learning for Sequential Personalization of Small Language Models: A Stability Monitoring Analysis

Thomas S. Paula, Lucas S. Kupssinskü, Rodrigo C. Barros

Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locally personalized applications.…

cs.LG2026

Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil

Wolfgang R. Rowell, Lucas S. Kupssinskü

The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP). While these data-driven architectures demons…

cs.LG2026

Quantization-Robust LLM Unlearning via Low-Rank Adaptation

João Vitor Boer Abitante, Joana Meneguzzo Pasquali, Luan Fonseca Garcia +4

Large Language Model (LLM) unlearning aims to remove targeted knowledge from a trained model, but practical deployments often require post-training quantization (PTQ) for efficient…

cs.LG2022★ 44 cited

Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey

Otávio Parraga, Martin D. More, Christian M. Oliveira +6

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of t…