23,461 research outputs found
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
How to develop slim and accurate deep neural networks has become crucial for
real- world applications, especially for those employed in embedded systems.
Though previous work along this research line has shown some promising results,
most existing methods either fail to significantly compress a well-trained deep
network or require a heavy retraining process for the pruned deep network to
re-boost its prediction performance. In this paper, we propose a new layer-wise
pruning method for deep neural networks. In our proposed method, parameters of
each individual layer are pruned independently based on second order
derivatives of a layer-wise error function with respect to the corresponding
parameters. We prove that the final prediction performance drop after pruning
is bounded by a linear combination of the reconstructed errors caused at each
layer. Therefore, there is a guarantee that one only needs to perform a light
retraining process on the pruned network to resume its original prediction
performance. We conduct extensive experiments on benchmark datasets to
demonstrate the effectiveness of our pruning method compared with several
state-of-the-art baseline methods
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net),
a very deep architecture where each stacked processing block is derived from a
time-invariant non-autonomous dynamical system. Non-autonomy is implemented by
skip connections from the block input to each of the unrolled processing stages
and allows stability to be enforced so that blocks can be unrolled adaptively
to a pattern-dependent processing depth. NAIS-Net induces non-trivial,
Lipschitz input-output maps, even for an infinite unroll length. We prove that
the network is globally asymptotically stable so that for every initial
condition there is exactly one input-dependent equilibrium assuming tanh units,
and multiple stable equilibria for ReL units. An efficient implementation that
enforces the stability under derived conditions for both fully-connected and
convolutional layers is also presented. Experimental results show how NAIS-Net
exhibits stability in practice, yielding a significant reduction in
generalization gap compared to ResNets.Comment: NIPS 201
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