The following pages link to PyTorch (Q32752):
Displaying 44 items.
- The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise (Q5131130) (← links)
- Stochastic Multichannel Ranking with Brain Dynamics Preferences (Q5131156) (← links)
- Toward Training Recurrent Neural Networks for Lifelong Learning (Q5131161) (← links)
- On Kernel Method–Based Connectionist Models and Supervised Deep Learning Without Backpropagation (Q5131164) (← links)
- Algorithm 1004 (Q5132311) (← links)
- Quant GANs: deep generation of financial time series (Q5139243) (← links)
- Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price (Q5147172) (← links)
- (Q5148994) (← links)
- (Q5149016) (← links)
- (Q5149253) (← links)
- DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
- An Inertial Newton Algorithm for Deep Learning (Q5159400) (← links)
- (Q5159435) (← links)
- (Q5159447) (← links)
- (Q5159471) (← links)
- (Q5159478) (← links)
- Understanding Graph Embedding Methods and Their Applications (Q5162644) (← links)
- The model reduction of the Vlasov–Poisson–Fokker–Planck system to the Poisson–Nernst–Planck system <i>via</i> the Deep Neural Network Approach (Q5163496) (← links)
- (Q5193680) (← links)
- Ensemble Kalman inversion: a derivative-free technique for machine learning tasks (Q5197869) (← links)
- Spectral Learning on Matrices and Tensors (Q5213205) (← links)
- Time-to-event prediction with neural networks and Cox regression (Q5214221) (← links)
- (Q5214290) (← links)
- Deep unfolding of a proximal interior point method for image restoration (Q5220306) (← links)
- Deep learning the holographic black hole with charge (Q5221253) (← links)
- BinaryRelax: A Relaxation Approach for Training Deep Neural Networks with Quantized Weights (Q5230408) (← links)
- Ultrametric fitting by gradient descent <sup>*</sup> (Q5857451) (← links)
- Multilevel Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data (Q5857926) (← links)
- Dynamical Variational Autoencoders: A Comprehensive Review (Q5863990) (← links)
- A dataset-free deep learning method for low-dose CT image reconstruction (Q5867675) (← links)
- Approximation of discontinuous inverse operators with neural networks (Q5867678) (← links)
- Optimal Energy Shaping via Neural Approximators (Q5868542) (← links)
- Online Component Analysis, Architectures and Applications (Q5870787) (← links)
- Deep Learning (Q5872922) (← links)
- Learning physical properties of anomalous random walks using graph neural networks (Q5874132) (← links)
- Universal characteristics of deep neural network loss surfaces from random matrix theory (Q5878969) (← links)
- Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process (Q5880409) (← links)
- On the impact of deep learning-based time-series forecasts on multistage stochastic programming policies (Q5883596) (← links)
- Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks (Q5885833) (← links)
- Graph Neural Networks for Natural Language Processing: A Survey (Q5885996) (← links)
- Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats (Q5885997) (← links)
- Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder (Q5886859) (← links)
- Computer vision. Algorithms and applications (Q5918475) (← links)
- Scale-covariant and scale-invariant Gaussian derivative networks (Q5918660) (← links)
