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Despite their impact on computer vision and face recognition, the inner workings of deep convolutional neural networks (CNNs) have traditionally been regarded as uninterpretable. We demonstrate this to be false by proposing prediction gradients to understand how neural networks encode concepts into individual units. In constrast, existing efforts to understand convolutional nets focus on visualizing...
We address the problem of computing joint sparse representation of visual signal across multiple kernel-based representations. Such a problem arises naturally in supervised visual recognition applications where one aims to reconstruct a test sample with multiple features from as few training subjects as possible. We cast the linear version of this problem into a multi-task joint covariate selection...
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