Maitraka dynasty pdf

This dissertation is an exploration of data-driven discovery, inspired by neuroscientific studies of the brain. Each of the three projects listed will describe a different domain of input data (self-driving video, medical image, and biological audio), and how investigating neural network behavior trained on that data can reveal insights for each underlying task. Chapter 2 assess motion selectivity in a self-driving network trained to predict two output tasks: steering and motor. We show how different control conditions can define temporal behavior, as well as how frame order is only implicitly learned if relevant for the task, even if the frame order is present in the input and output training data. Chapter 3 assesses self-supervisedly learned representations from retinal fundus images. We show how these learned representations can drive a voting scheme classifier to match supervised and human expert baselines for disease severity prediction in this field, minimizing the bias enforced from clinically relevant ground truth labels. These representations can be further probed to disc

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