The ability to anticipate tides is critical for a wide range of marine organisms, but this task is complicated by the diversity of tidal patterns on Earth. Previous findings suggest that organisms whose geographic range spans multiple types of tidal cycles can produce distinct patterns of rhythmic behavior that correspond to the tidal cycles they experience. How this behavioral plasticity is achieved, however, is unclear.
View Article and Find Full Text PDFInterpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI.
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