The following pages link to ImageNet (Q32917):
Displaying 50 items.
- One-trial correction of legacy AI systems and stochastic separation theorems (Q2213103) (← links)
- Affine symmetries and neural network identifiability (Q2214101) (← links)
- Image-based material characterization of complex microarchitectured additively manufactured structures (Q2214430) (← links)
- BCR-net: A neural network based on the nonstandard wavelet form (Q2214634) (← links)
- Nonparametric regression using deep neural networks with ReLU activation function (Q2215715) (← links)
- Bonsai: diverse and shallow trees for extreme multi-label classification (Q2217402) (← links)
- Spanning attack: reinforce black-box attacks with unlabeled data (Q2217425) (← links)
- Ada-boundary: accelerating DNN training via adaptive boundary batch selection (Q2217442) (← links)
- On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning (Q2218090) (← links)
- Optimization for deep learning: an overview (Q2218095) (← links)
- A review on deep learning in medical image reconstruction (Q2218098) (← links)
- Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network (Q2219521) (← links)
- Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
- Accelerating flash calculation through deep learning methods (Q2222279) (← links)
- A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
- Solving electrical impedance tomography with deep learning (Q2223016) (← links)
- Deep multiscale model learning (Q2223279) (← links)
- Coercing machine learning to output physically accurate results (Q2223280) (← links)
- Bag similarity network for deep multi-instance learning (Q2225179) (← links)
- Semi-supervised semantic mapping through label propagation with semantic texture meshes (Q2226183) (← links)
- Self-supervised model adaptation for multimodal semantic segmentation (Q2226186) (← links)
- Representation learning on unit ball with 3D roto-translational equivariance (Q2226187) (← links)
- Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media (Q2226818) (← links)
- Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
- SyReNN: a tool for analyzing deep neural networks (Q2233513) (← links)
- Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (Q2236964) (← links)
- A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM (Q2237268) (← links)
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
- Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition (Q2237770) (← links)
- Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
- A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (Q2237801) (← links)
- Multiple object tracking: a literature review (Q2238613) (← links)
- Embedding deep networks into visual explanations (Q2238677) (← links)
- Reward is enough (Q2238710) (← links)
- A statistician teaches deep learning (Q2241468) (← links)
- A noise-robust online convolutional coding model and its applications to Poisson denoising and image fusion (Q2242520) (← links)
- A weight initialization based on the linear product structure for neural networks (Q2247166) (← links)
- Learning semantic representations of objects and their parts (Q2251465) (← links)
- Optimization of robust loss functions for weakly-labeled image taxonomies (Q2254258) (← links)
- SO(8) supergravity and the magic of machine learning (Q2273413) (← links)
- A survey of randomized algorithms for training neural networks (Q2282875) (← links)
- Differentiation of recurrence from radiation necrosis in gliomas based on the radiomics of combinational features and multimodality MRI images (Q2283749) (← links)
- A faster algorithm for reducing the computational complexity of convolutional neural networks (Q2283825) (← links)
- Training a multilayer network with low-memory kernel-and-range projection (Q2291074) (← links)
- Urban acoustic classification based on deep feature transfer learning (Q2291086) (← links)
- Benchmarking machine-learning software and hardware for quantitative economics (Q2291794) (← links)
- Accelerating deep neural network training with inconsistent stochastic gradient descent (Q2292210) (← links)
- Limitations of shallow nets approximation (Q2292226) (← links)
- Few-shot learning in deep networks through global prototyping (Q2292229) (← links)
- Utilizing relevant RGB-D data to help recognize RGB images in the target domain (Q2299167) (← links)
