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Autoencoder in Autoencoder Networks
IEEE Transactions on Neural Networks and Learning SystemsModeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets).
Changqing Zhang 0002 +5 more
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2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019
This paper proposes a novel deep reinforcement learning (RL) architecture, which learns a dynamics model in latent space that is behaviorally grounded to the observed space and applies the framework of MDP homomorphisms to provide bounds for the loss in performance. In contrast to traditional model based reinforcement learning algorithms, this approach
Sourabh Bose, Manfred Huber
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This paper proposes a novel deep reinforcement learning (RL) architecture, which learns a dynamics model in latent space that is behaviorally grounded to the observed space and applies the framework of MDP homomorphisms to provide bounds for the loss in performance. In contrast to traditional model based reinforcement learning algorithms, this approach
Sourabh Bose, Manfred Huber
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Neurocomputing, 2014
This paper presents a training method that encodes each word into a different vector in semantic space and its relation to low entropy coding. Elman network is employed in the method to process word sequences from literary works. The trained codes possess reduced entropy and are used in ranking, indexing, and categorizing literary works. A modification
Cheng-Yuan Liou +3 more
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This paper presents a training method that encodes each word into a different vector in semantic space and its relation to low entropy coding. Elman network is employed in the method to process word sequences from literary works. The trained codes possess reduced entropy and are used in ranking, indexing, and categorizing literary works. A modification
Cheng-Yuan Liou +3 more
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Artificial Life
Abstract This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward
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Abstract This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward
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Variational Autoencoders for Data Augmentation in Clinical Studies
Applied Sciences (Switzerland), 2023Vangelis D Karalis
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Using Autoencoders for Anomaly Detection and Transfer Learning in IoT
Computers, 2021Jenq-Haur Wang
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Analysis of Autoencoders for Network Intrusion Detection
Sensors, 2021Yun-Gyung Cheong +2 more
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FastGAE: Scalable graph autoencoders with stochastic subgraph decoding
Neural Networks, 2021Michalis Vazirgiannis +2 more
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