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Reconstructing attractors with autoencoders
Chaos: An Interdisciplinary Journal of Nonlinear ScienceWe propose a method based on autoencoders to reconstruct attractors from recorded footage, preserving the topology of the underlying phase space. We provide theoretical support and test the method with (i) footage of the temperature and stream function fields involved in the Lorenz atmospheric convection problem and (ii) a time series obtained by ...
F. Fainstein, G. B. Mindlin, P. Groisman
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Multivariate Variational Autoencoder
CoRRLearning latent representations that are simultaneously expressive, geometrically well-structured, and reliably calibrated remains a central challenge for Variational Autoencoders (VAEs). Standard VAEs typically assume a diagonal Gaussian posterior, which simplifies optimization but rules out correlated uncertainty and often yields entangled or ...
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2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2022
The Retinex algorithms find wide applications as image enhancers, for their capability of preserving edges, while at the same time attenuating smooth gradients and chromatic dominants. They are characterized by the fact that the output chromatic intensity of a pixel is not determined in isolation (or looking only at the contiguous pixels) but through ...
Pezzoni, Claudio +3 more
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The Retinex algorithms find wide applications as image enhancers, for their capability of preserving edges, while at the same time attenuating smooth gradients and chromatic dominants. They are characterized by the fact that the output chromatic intensity of a pixel is not determined in isolation (or looking only at the contiguous pixels) but through ...
Pezzoni, Claudio +3 more
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AE2-Nets: Autoencoder in Autoencoder Networks
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks.
Changqing Zhang 0002 +2 more
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2021
Variational AutoEncoder (VAE) as a class of neural networks performing nonlinear dimensionality reduction has become an effective tool in neuroimaging analysis. Currently, most studies on VAE consider unsupervised learning to capture the latent representations and to some extent, this strategy may be under-explored in the case of heavy noise and ...
Mingli Zhang +7 more
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Variational AutoEncoder (VAE) as a class of neural networks performing nonlinear dimensionality reduction has become an effective tool in neuroimaging analysis. Currently, most studies on VAE consider unsupervised learning to capture the latent representations and to some extent, this strategy may be under-explored in the case of heavy noise and ...
Mingli Zhang +7 more
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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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