Pages that link to "Item:Q3101410"

The following pages link to Reducing the Dimensionality of Data with Neural Networks (Q3101410):

Displaying 50 items.

• A game-theoretic analysis of deep neural networks (Q2151381) (← links)
• Proper orthogonal decomposition and physical field reconstruction with artificial neural networks (ANN) for supercritical flow problems (Q2161608) (← links)
• Challenges for machine learning in RNA-protein interaction prediction (Q2162489) (← links)
• Autoencoders reloaded (Q2165369) (← links)
• Model order reduction for compressible flows solved using the discontinuous Galerkin methods (Q2168284) (← links)
• How can we identify the sparsity structure pattern of high-dimensional data: an elementary statistical analysis to interpretable machine learning (Q2170515) (← links)
• A homotopy gated recurrent unit for predicting high dimensional hyperchaos (Q2170813) (← links)
• A survey of outlier detection in high dimensional data streams (Q2172855) (← links)
• On a high-dimensional model representation method based on copulas (Q2178128) (← links)
• ASD+M: automatic parameter tuning in stochastic optimization and on-line learning (Q2179079) (← links)
• Multilayer bootstrap networks (Q2179833) (← links)
• The growing curvilinear component analysis (GCCA) neural network (Q2179840) (← links)
• Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429) (← links)
• A deep belief network with PLSR for nonlinear system modeling (Q2181070) (← links)
• On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-bipolar restricted Boltzmann machines (Q2181099) (← links)
• An adaptive deep Q-learning strategy for handwritten digit recognition (Q2182881) (← links)
• Understanding autoencoders with information theoretic concepts (Q2185600) (← links)
• Online sequential echo state network with sparse RLS algorithm for time series prediction (Q2185621) (← links)
• An unsupervised parameter learning model for RVFL neural network (Q2188218) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties (Q2192791) (← links)
• Deep learning for generic object detection: a survey (Q2193851) (← links)
• Relational intelligence recognition in online social networks -- a survey (Q2197797) (← links)
• Soft sensor modeling of key effluent parameters in wastewater treatment process based on SAE-NN (Q2199925) (← links)
• Establishing simple relationship between eigenvector and matrix elements (Q2213226) (← links)
• Optimization for deep learning: an overview (Q2218095) (← links)
• Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning (Q2222332) (← links)
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (Q2223001) (← links)
• Data-space inversion using a recurrent autoencoder for time-series parameterization (Q2225357) (← links)
• A machine-learning approach to synthesize virtual sensors for parameter-varying systems (Q2235477) (← links)
• Nonequilibrium thermodynamics of self-supervised learning (Q2236536) (← links)
• Adversarial domain adaptation network for tumor image diagnosis (Q2237197) (← links)
• Parametric non-intrusive model order reduction for flow-fields using unsupervised machine learning (Q2237497) (← links)
• A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness (Q2237726) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• Deep learning for credit scoring: do or don't? (Q2239871) (← links)
• Data-driven reduced bond graph for nonlinear multiphysics dynamic systems (Q2244143) (← links)
• Machine learning-combined topology optimization for functionary graded composite structure design (Q2246382) (← links)
• Comments on ``Data science, big data and statistics'' (Q2273156) (← links)
• A survey of randomized algorithms for training neural networks (Q2282875) (← links)
• Intelligent fuzzy sliding mode control for complex robot system with disturbances (Q2291145) (← links)
• Nonredundant sparse feature extraction using autoencoders with receptive fields clustering (Q2292197) (← links)
• Deep neural mapping support vector machines (Q2292203) (← links)
• A linear relation between input and first layer in neural networks (Q2294577) (← links)
• A DBN-based deep neural network model with multitask learning for online air quality prediction (Q2303386) (← links)
• Solving for high-dimensional committor functions using artificial neural networks (Q2319851) (← links)
• Deep collective matrix factorization for augmented multi-view learning (Q2320570) (← links)
• Improving latent variable descriptiveness by modelling rather than ad-hoc factors (Q2320589) (← links)
• Feature extraction from telematics car driving heatmaps (Q2323654) (← links)
• Diffusion nets (Q2325536) (← links)