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Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality
Using the dual view, in this paper, we rethink the conventional interpretations of DNNs thereby explicitsing the implicit interpretability of DNNs.
Disentangling deep neural networks with rectified linear units using duality
To address `black box'-ness, we propose a novel interpretable counterpart of DNNs with ReLUs namely deep linearly gated networks (DLGN): the pre-activations to the gates are generated by a deep linear network, and the gates are then applied as external masks to learn the weights in a different network.
Deep Learning Is Composite Kernel Learning
Recent works have connected deep learning and kernel methods.
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
To this end, we encode the on/off state of the gates of a given input in a novel 'neural path feature' (NPF), and the weights of the DNN are encoded in a novel 'neural path value' (NPV).
Deep Gated Networks: A framework to understand training and generalisation in deep learning
In DGNs, a single neuronal unit has two components namely the pre-activation input (equal to the inner product the weights of the layer and the previous layer outputs), and a gating value which belongs to $[0, 1]$ and the output of the neuronal unit is equal to the multiplication of pre-activation input and the gating value.
