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Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
Simon S. Du, Wei Hu, Jason D. Lee
Neural Information Processing Systems http://nips.cc/
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
Key to the application of such models in safety-critical domains is the quantification of their model error. Gaussian processes provide such a measure anduniform error bounds havebeen derived,which allowsafe control based on thesemodels.
Zero-shot Knowledge Transfer via Adversarial Belief Matching
Paul Micaelli, Amos J. Storkey
However,duetogrowing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. We propose a novel method which trains a student to match the predictions of its teacher without using anydata ormetadata. Weachievethisbytraining anadversarial generator to search for images on which the student poorly matches the teacher, and then using them to train the student.
Dual Swap Disentangling
Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, Dacheng Tao, Mingli Song
Neural Information Processing Systems http://nips.cc/
A Stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, Robert Scheichl
Neural Information Processing Systems http://nips.cc/
A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
Neural Information Processing Systems http://nips.cc/