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.
- A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence (Q2328425) (← links)
- A novel parallel auto-encoder framework for multi-scale data in civil structural health monitoring (Q2331629) (← links)
- Automatic prediction of meningioma grade image based on data amplification and improved convolutional neural network (Q2332568) (← links)
- Provable ICA with unknown Gaussian noise, and implications for Gaussian mixtures and autoencoders (Q2345948) (← links)
- Learning flexible sensori-motor mappings in a complex network (Q2376421) (← links)
- \(p\)-adic mathematical physics: the first 30 years (Q2403576) (← links)
- Hierarchical semi-Markov conditional random fields for deep recursive sequential data (Q2407880) (← links)
- Cost-sensitive sequential three-way decision modeling using a deep neural network (Q2409099) (← links)
- Dynamical analysis of contrastive divergence learning: restricted Boltzmann machines with Gaussian visible units (Q2418189) (← links)
- Diversity loss is predicted to increase extinction risk of specialist animals by constraining their ability to expand niche (Q2423944) (← links)
- Large data sets and machine learning: applications to statistical arbitrage (Q2424788) (← links)
- Analyzing market baskets by restricted Boltzmann machines (Q2454362) (← links)
- Neighbourhood-preserving dimension reduction via localised multidimensional scaling (Q2636500) (← links)
- Autoencoder asset pricing models (Q2658795) (← links)
- A survey of deep network techniques all classifiers can adopt (Q2659274) (← links)
- Deep relevant representation learning for soft sensing (Q2660714) (← links)
- Machine learning based data retrieval for inverse scattering problems with incomplete data (Q2660886) (← links)
- Learning nonlinear state-space models using autoencoders (Q2665158) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- Quantum SUSAN edge detection based on double chains quantum genetic algorithm (Q2675913) (← links)
- A physically constrained variational autoencoder for geochemical pattern recognition (Q2676496) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation (Q2678544) (← links)
- Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
- \(p\)-adic statistical field theory and deep belief networks (Q2685103) (← links)
- A study on the effects of normalized TSP features for automated algorithm selection (Q2699964) (← links)
- The Whitney reduction network: A method for computing autoassociative graphs (Q2784824) (← links)
- Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks (Q2810824) (← links)
- Information Preserving Dimensionality Reduction (Q2835632) (← links)
- Semisupervised multimodal dimensionality reduction (Q2857287) (← links)
- An efficient learning procedure for deep Boltzmann machines (Q2919431) (← links)
- Learning where to attend with deep architectures for image tracking (Q2919435) (← links)
- Independent component analysis: recent advances (Q2955464) (← links)
- Large-Margin Classification in Infinite Neural Networks (Q3057218) (← links)
- Comparing Classification Methods for Longitudinal fMRI Studies (Q3057223) (← links)
- Supervised neural computing and LMI optimisation for order model reduction-based control of the Buck switching-mode power supply (Q3082650) (← links)
- Bounding the Bias of Contrastive Divergence Learning (Q3085329) (← links)
- The Architectures of Geoffrey Hinton (Q3296987) (← links)
- Deep Variational Inference (Q3300544) (← links)
- Computational modelling of memory retention from synapse to behaviour (Q3301546) (← links)
- Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses (Q3303061) (← links)
- Machine learning algorithms based on generalized Gibbs ensembles (Q3303207) (← links)
- Resolution and relevance trade-offs in deep learning (Q3303274) (← links)
- Neural-Network Quantum State of Transverse-Field Ising Model (Q3387540) (← links)
- Low Rank Tensor Manifold Learning (Q3449322) (← links)
- Cellular Automata for Efficient Image and Video Compression (Q3454780) (← links)
- Contrastive Divergence in Gaussian Diffusions (Q3519233) (← links)
- Deep, Narrow Sigmoid Belief Networks Are Universal Approximators (Q3536226) (← links)
- Prototype Classification: Insights from Machine Learning (Q3612119) (← links)
- VISUAL HAND GESTURES CLASSIFICATION USING WAVELET TRANSFORMS (Q4474561) (← links)
