Pages that link to "Item:Q3101410"
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The following pages link to Reducing the Dimensionality of Data with Neural Networks (Q3101410):
Displaying 50 items.
- Tensorized feature extraction technique for multimodality preserving manifold visualization (Q1932987) (← links)
- Complex-valued autoencoders (Q1941595) (← links)
- On the equivalence of Hopfield networks and Boltzmann machines (Q1941600) (← links)
- Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition (Q1942726) (← links)
- Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis (Q1945126) (← links)
- Semi-supervised local Fisher discriminant analysis for dimensionality reduction (Q1959540) (← links)
- Generative adversarial networks with decoder-encoder output noises (Q1982397) (← links)
- Training of deep neural networks for the generation of dynamic movement primitives (Q1982413) (← links)
- Multi-feature gait recognition with DNN based on sEMG signals (Q1984095) (← links)
- On separating points by lines (Q1985301) (← links)
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
- Thermodynamics of restricted Boltzmann machines and related learning dynamics (Q1990123) (← links)
- Convergence of contrastive divergence algorithm in exponential family (Q1991693) (← links)
- A cycle deep belief network model for multivariate time series classification (Q1993338) (← links)
- A factor graph model for unsupervised feature selection (Q2004730) (← links)
- Improved neural networks based on mutual information via information geometry (Q2004876) (← links)
- A hybrid autoencoder network for unsupervised image clustering (Q2004903) (← links)
- Non-iterative and fast deep learning: multilayer extreme learning machines (Q2005317) (← links)
- Face recognition via deep stacked denoising sparse autoencoders (DSDSA) (Q2009382) (← links)
- Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network (Q2020836) (← links)
- Deep learning for model order reduction of multibody systems to minimal coordinates (Q2020838) (← links)
- Towards blending physics-based numerical simulations and seismic databases using generative adversarial network (Q2021045) (← links)
- The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164) (← links)
- A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites (Q2021276) (← links)
- Hierarchical deep-learning neural networks: finite elements and beyond (Q2033626) (← links)
- Efficient regularized spectral data embedding (Q2036143) (← links)
- Deep graph similarity learning: a survey (Q2036720) (← links)
- A novel semi-supervised multi-view clustering framework for screening Parkinson's disease (Q2038734) (← links)
- Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
- Multi-aspect renewable energy forecasting (Q2055561) (← links)
- Guest editorial: Generative adversarial networks for computer vision (Q2056078) (← links)
- A theory of incremental compression (Q2056272) (← links)
- Deep learning on image denoising: an overview (Q2057732) (← links)
- Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation (Q2059122) (← links)
- A pore space reconstruction method of shale based on autoencoders and generative adversarial networks (Q2065844) (← links)
- Network embedding: taxonomies, frameworks and applications (Q2065964) (← links)
- Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
- Dimension reduction in recurrent networks by canonicalization (Q2076953) (← links)
- GLRM: logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy (Q2077014) (← links)
- Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression (Q2083714) (← links)
- Learning large \(Q\)-matrix by restricted Boltzmann machines (Q2088924) (← links)
- A game-theoretic perspective of deep neural networks (Q2098170) (← links)
- A capsule-unified framework of deep neural networks for graphical programming (Q2099868) (← links)
- An iterative stacked weighted auto-encoder (Q2099909) (← links)
- \(\pi\) VAE: a stochastic process prior for Bayesian deep learning with MCMC (Q2103969) (← links)
- Deep random walk of unitary invariance for large-scale data representation (Q2124155) (← links)
- Image inversion and uncertainty quantification for constitutive laws of pattern formation (Q2131064) (← links)
- A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media (Q2132604) (← links)
- Neural network training using \(\ell_1\)-regularization and bi-fidelity data (Q2138992) (← links)
- Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130) (← links)
