nullw
Learning Distributions Generated by One-Layer ReLU Networks
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
Neural Information Processing Systems http://nips.cc/
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On the Power and Limitations of Random Features for Understanding Neural Networks
Considering the one-hidden-layer example above, this corresponds to learning linear predictors over a fixed representation (chosen obliviously and randomly at initialization).
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If You Want to Be Robust, Be Wary of Initialization
We introduce a theoretical framework bridging the connection between initialization strategies and a network's resilience to adversarial perturbations.
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