inductive bias
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representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other
Co-first author order is arbitrary and may be swapped when citing this work.
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Scaling MLPs: AT ale of Inductive Bias
Empirical insights into MLPs are important for multiple reasons.
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Conditional independence testing under misspecified inductive biases
Then, we study the performance of regression-based CI tests under misspecified inductive biases.
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Learning Interpretable Low-dimensional Representation via Physical Symmetry
However, most methods rely heavily on music domain knowledge.
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