Search results with tag "Deep networks"
Communication-Efficient Learning of Deep Networks from ...
proceedings.mlr.pressBoth of these tasks are well-suited to learning a neural net-work. For image classification feed-forward deep networks, and in particular convolutional networks, are well-known to provide state-of-the-art results [26, 25]. For language modeling tasks recurrent neural networks, and in particular LSTMs, have achieved state-of-the-art results [20 ...
Learning Transferable Features with Deep Adaptation Networks
proceedings.mlr.pressdeep networks, resulting in statistically unboundedrisk for target tasks (Mansour et al., 2009; Ben-David et al., 2010). Our work is primarily motivated by Yosinski et al. (2014), which comprehensively explores feature transferability of deep convolutional neural networks. The method focuses on a different scenario where the learning tasks are ...
Understanding the difficulty of training deep feedforward ...
proceedings.mlr.pressdeep networks with sigmoids but initialized from unsuper-vised pre-training (e.g. from RBMs) do not suffer from this saturation behavior. Our proposed explanation rests on the hypothesis that the transformation that the lower layers of the randomly initialized network computes initially is
Deep Residual Learning for Image Recognition
arxiv.orgDeep convolutional neural networks [22,21] have led to a series of breakthroughs for image classification [21, 50,40]. Deep networks naturally integrate low/mid/high-level features [50] and classifiers in an end-to-end multi-layer fashion, and the “levels” of features can be enriched by the number of stacked layers (depth). Recent evidence