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Lily Zhang

2 accepted papers

2022

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets

ICML 2022spotlight

Permutation invariant neural networks are a promising tool for predictive modeling of set data. We show, however, that existing architectures struggle to perform well when they are deep. In this work, we mathematically and empirically analyze normalization layers and residual connections in the cont…

2021

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

ICML 2021spotlight

Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD images than images from the training distribution. In this work, we explain why this behavior should be attributed to mo…

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