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Found 4 papers, 2 papers with code
Neuron to Graph: Interpreting Language Model Neurons at Scale
Conventional methods require examination of examples with strong neuron activation and manual identification of patterns to decipher the concepts a neuron responds to.
N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
Understanding the function of individual neurons within language models is essential for mechanistic interpretability research.
REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology
Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows.
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology
Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance.
