The following pages link to ImageNet (Q32917):
Displaying 50 items.
- Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (Q2127250) (← links)
- Bi-shifting semantic auto-encoder for zero-shot learning (Q2127478) (← links)
- Monte Carlo method for estimating eigenvalues using error balancing (Q2128473) (← links)
- Analytic continuation of noisy data using Adams Bashforth residual neural network (Q2129155) (← links)
- Solving inverse-PDE problems with physics-aware neural networks (Q2129334) (← links)
- Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows (Q2129550) (← links)
- Construction of symmetric orthogonal designs with deep Q-network and orthogonal complementary design (Q2129592) (← links)
- Deep reinforcement learning for the control of conjugate heat transfer (Q2131088) (← links)
- Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting) (Q2131208) (← links)
- On obtaining sparse semantic solutions for inverse problems, control, and neural network training (Q2132578) (← links)
- A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media (Q2132604) (← links)
- Role of sparsity and structure in the optimization landscape of non-convex matrix sensing (Q2133410) (← links)
- Multi-objective CFD-driven development of coupled turbulence closure models (Q2133604) (← links)
- Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems (Q2133766) (← links)
- Self-adaptive deep neural network: numerical approximation to functions and PDEs (Q2133768) (← links)
- Physics-informed neural networks for the shallow-water equations on the sphere (Q2133783) (← links)
- DeLISA: deep learning based iteration scheme approximation for solving PDEs (Q2134800) (← links)
- Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
- A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networks (Q2135256) (← links)
- Prediction of hereditary cancers using neural networks (Q2135373) (← links)
- Convergence rates of deep ReLU networks for multiclass classification (Q2137813) (← links)
- Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning (Q2139011) (← links)
- Deep learning based classification of time series of Chen and Rössler chaotic systems over their graphic images (Q2140130) (← links)
- SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
- Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130) (← links)
- Construction of discontinuity detectors using convolutional neural networks (Q2147452) (← links)
- Deep learning for the partially linear Cox model (Q2148978) (← links)
- Error estimates for deep learning methods in fluid dynamics (Q2149063) (← links)
- Deep neural network for drawing networks, \({(DNN)^{ 2 }} \) (Q2151466) (← links)
- Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480) (← links)
- ReLU deep neural networks from the hierarchical basis perspective (Q2159911) (← links)
- Machine learning for topology optimization: physics-based learning through an independent training strategy (Q2160385) (← links)
- Transfer learning on T1-weighted images for brain age estimation (Q2160780) (← links)
- A statistical descriptor for texture images based on the box counting fractal dimension (Q2161961) (← links)
- Challenges in Markov chain Monte Carlo for Bayesian neural networks (Q2163079) (← links)
- Scrutinizing XAI using linear ground-truth data with suppressor variables (Q2163233) (← links)
- Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
- Diagnosis of breast cancer for modern mammography using artificial intelligence (Q2168117) (← links)
- Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
- Training thinner and deeper neural networks: jumpstart regularization (Q2170213) (← links)
- Manifold regularization based on Nyström type subsampling (Q2175018) (← links)
- Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (Q2176735) (← links)
- Learning in the machine: the symmetries of the deep learning channel (Q2179077) (← links)
- Modular representation of layered neural networks (Q2179094) (← links)
- Deep neural networks for texture classification -- a theoretical analysis (Q2179105) (← links)
- LRC-Net: learning discriminative features on point clouds by encoding local region contexts (Q2180642) (← links)
- Optimal approximation of piecewise smooth functions using deep ReLU neural networks (Q2182898) (← links)
- Accelerating deep learning with memcomputing (Q2182923) (← links)
- A hybrid deep computation model for feature learning on aero-engine data: applications to fault detection (Q2182985) (← links)
- Gradient descent optimizes over-parameterized deep ReLU networks (Q2183586) (← links)
