6 citations · 6 across the 6 of their papers we have counts for
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cs.LG2023
Understanding Addition in Transformers
Philip Quirke, Fazl Barez
Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use. This paper provides a comprehensive analysis of a one-layer T…
cs.CV2023★ 6 cited
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West +28
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of micr…