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Evaluating the visualization of what a deep neural network has learned - 2016.pdf

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Evaluating the visualization of what a deep neural network has learned - 2016.pdf
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding.pdf
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Improving interpretability of deep neural networks with semantic information - 2017.pdf
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Automatic rule extraction from long short term memory (LSTM) networks - 2017.pdf
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Increasing the interpretability of recurrent neural networks using hidden Markov models - 2016.pdf
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Interpretable recurrent neural networks using sequential sparse recovery - 2017.pdf
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A powerful generative model using random weights for the deep image representation - 2016.pdf
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Memory visualization for gated recurrent neural networks in speech recognition - 2017.pdf
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A new method to visualize deep neural networks - 2017.pdf
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Methods for interpreting and understanding deep neural networks - 2017.pdf
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Node-by-node greedy deep learning for interpretable features - 2016.pdf
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Rationalizing neural predictions - 2016.pdf
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size - 2016.pdf
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Stimulated deep neural network for speech recognition - 2016.pdf
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Strongly-typed recurrent neural networks - 2016.pdf
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks - 2016.pdf
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Towards better analysis of deep convolutional neural networks - 2016.pdf
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Understanding neural networks through deep visualization - 2015.pdf
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Visual analysis of hidden state dynamics in recurrent neural networks - 2016.pdf
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Visualizing LSTM decisions - 2017.pdf
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