An algorithm for recognition of n-collapsing words

AbstractA word w over a finite alphabet Σ is n-collapsing if for an arbitrary deterministic finite automaton A=〈Q,Σ,δ〉, the inequality |δ(Q,w)|≤|Q|−n holds provided that |δ(Q,u)|≤|Q|−n for some word u∈Σ+ (depending on A). We prove that the property of n-collapsing is algorithmically recognizable for any given positive integer n. We also prove that the language of all n-collapsing words is context-sensitive

From neural PCA to deep unsupervised learning

A network supporting deep unsupervised learning is presented. The network is an autoencoder with lateral shortcut connections from the encoder to decoder at each level of the hierarchy. The lateral shortcut connections allow the higher levels of the hierarchy to focus on abstract invariant features. While standard autoencoders are analogous to latent variable models with a single layer of stochastic variables, the proposed network is analogous to hierarchical latent variables models. Learning combines denoising autoencoder and denoising sources separation frameworks. Each layer of the network contributes to the cost function a term which measures the distance of the representations produced by the encoder and the decoder. Since training signals originate from all levels of the network, all layers can learn efficiently even in deep networks. The speedup offered by cost terms from higher levels of the hierarchy and the ability to learn invariant features are demonstrated in experiments.Comment: A revised version of an article that has been accepted for publication in Advances in Independent Component Analysis and Learning Machines (2015), edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen and Jouko Lampine