The following pages link to PyTorch (Q32752):
Displaying 50 items.
- Learning from missing data with the binary latent block model (Q2066751) (← links)
- Three-dimensional structural geological modeling using graph neural networks (Q2066838) (← links)
- Superquantiles at work: machine learning applications and efficient subgradient computation (Q2070410) (← links)
- Incorporating symbolic domain knowledge into graph neural networks (Q2071312) (← links)
- Beyond graph neural networks with lifted relational neural networks (Q2071316) (← links)
- MLife: a lite framework for machine learning lifecycle initialization (Q2071479) (← links)
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- A Bayesian multiscale deep learning framework for flows in random media (Q2072635) (← links)
- Learning landmark geodesics using the ensemble Kalman filter (Q2072666) (← links)
- A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
- Optimal staggered-grid finite-difference method for wave modeling based on artificial neural networks (Q2074113) (← links)
- Joint and individual analysis of breast cancer histologic images and genomic covariates (Q2078283) (← links)
- How does momentum benefit deep neural networks architecture design? A few case studies (Q2079522) (← links)
- Fully hyperbolic convolutional neural networks (Q2079530) (← links)
- Progressive-encoding-based transmission for DNN-enabled edge intelligence in unreliable network (Q2079869) (← links)
- Variational inference and sparsity in high-dimensional deep Gaussian mixture models (Q2080343) (← links)
- Improving bridge estimators via \(f\)-GAN (Q2080347) (← links)
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
- Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
- Machine learning for predicting microfluidic droplet generation properties (Q2084128) (← links)
- An upgraded-YOLO with object augmentation: mini-UAV detection under low-visibility conditions by improving deep neural networks (Q2084315) (← links)
- Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph (Q2084652) (← links)
- A sequential addition and migration method for generating microstructures of short fibers with prescribed length distribution (Q2086038) (← links)
- Purity assessment of pellets using deep learning (Q2086298) (← links)
- wsGAT: weighted and signed graph attention networks for link prediction (Q2086612) (← links)
- Deep reinforcement learning for \textsf{FlipIt} security game (Q2086680) (← links)
- Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm (Q2087196) (← links)
- Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise (Q2088244) (← links)
- Uncertainty quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball (Q2088318) (← links)
- Adaptive numerical dissipation control for high-order \(k\)-exact reconstruction schemes on vertex-centered unstructured grids using artificial neural networks (Q2088363) (← links)
- Deep evidential fusion network for medical image classification (Q2092457) (← links)
- High-performance statistical computing in the computing environments of the 2020s (Q2092893) (← links)
- Convolutional spectral kernel learning with generalization guarantees (Q2093403) (← links)
- A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem (Q2095152) (← links)
- Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery (Q2095535) (← links)
- Stochastic modeling of inhomogeneities in the aortic wall and uncertainty quantification using a Bayesian encoder-decoder surrogate (Q2096832) (← links)
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: extension to geometrical parameterizations (Q2096859) (← links)
- Deep learning based non-intrusive load monitoring with low resolution data from smart meters (Q2097964) (← links)
- \texttt{CAMERA}: a method for cost-aware, adaptive, multifidelity, efficient reliability analysis (Q2099759) (← links)
- Learning-based vs model-free adaptive control of a MAV under wind gust (Q2101765) (← links)
- Traditional and context-specific spam detection in low resource settings (Q2102320) (← links)
- Large scale tensor regression using kernels and variational inference (Q2102330) (← links)
- Semi-supervised semantic segmentation in Earth observation: the MiniFrance suite, dataset analysis and multi-task network study (Q2102368) (← links)
- An adaptive Polyak heavy-ball method (Q2102380) (← links)
- Recursive tree grammar autoencoders (Q2102393) (← links)
- Physics-informed neural networks for shell structures (Q2102673) (← links)
