665 citations · 4.3k across the 60 of their papers we have counts for
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Differentiating through the Fréchet Mean
Aaron Lou, Isay Katsman, Qingxuan Jiang +3
Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold. One possible exten…
Adversarial Example Decomposition
Horace He, Aaron Lou, Qingxuan Jiang +3
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer acr…
Bayesian representation learning with oracle constraints
Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch
Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy. Recently, high-dimensional parametric models like neural…