Inductive Biases in Deep Learning: Understanding Feature Representation – MarkTechPost
Machine learning research aims to learn representations that enable effective downstream task performance. A growing subfield seeks to interpret these representations’ roles in model behaviors or modify them to enhance alignment, interpretability, or generalization. Similarly, neuroscience examines neural representations and their behavioral correlations. Both fields focus on understanding or improving system computations, abstract behavior patterns on tasks, and their implementations. The relationship between representation and computation is complex and needs to be more straightforward. Highly over-parameterized deep networks often generalize well despite their capacity for memorization, suggesting an implicit inductive…
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