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Stacked Denoising Autoencoders: Learning Useful ...
thus bridging the performance gap with deep belief networks (DBN), and in several cases surpass-ing it. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative exp eriments show that, contrary to ordi-
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Domain-Adversarial Training of Neural Networks
www.jmlr.orgcross-domain transfer is one for which an algorithm cannot learn to identify the domain of ... trian image descriptors that are suitable for retrieval and veri cation. We apply domain- ... While the above approaches perform unsupervised domain adaptation, there are ap- ...
Random Search for Hyper-Parameter Optimization
www.jmlr.orgRANDOM SEARCH FOR HYPER-PARAMETER OPTIMIZATION search is used to identify regions in Λthat are promising and to develop the intuition necessary to choose the sets L(k).A major drawback of manual search is the difficulty in reproducing results.
Neural Architecture Search: A Survey
www.jmlr.orgDeep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect ... operations like depthwise separable convolutions (Chollet, 2016) or dilated convolutions (Yu
Weisfeiler-Lehman Graph Kernels
www.jmlr.orgWeisfeiler-Lehman sequence of graphs, including a highly efficient kernel comparing subtree-like patterns. Its runtime scales only linearly in the number of edges of the graphs and the length of the Weisfeiler-Lehman graph sequence. In our experimentalevaluation, our kernels outperform
Adaptive Subgradient Methods for Online Learning and ...
www.jmlr.orgKeywords: subgradient methods, adaptivity, online learning, stochastic convex optimization 1. Introduction In many applications of online and stochastic learning, the input instances are of very high di-mension, yet within any particular instance only a …
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