The following pages link to PyTorch (Q32752):
Displaying 50 items.
- HyperNOMAD (Q5025212) (← links)
- Explainable Deep Learning: A Field Guide for the Uninitiated (Q5026262) (← links)
- Probabilistic machine learning. An introduction (Q5030377) (← links)
- Computing Large Market Equilibria Using Abstractions (Q5031015) (← links)
- Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning (Q5041623) (← links)
- Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
- (Q5043213) (← links)
- Bellman's principle of optimality and deep reinforcement learning for time-varying tasks (Q5043501) (← links)
- Deep Generation of Coq Lemma Names Using Elaborated Terms (Q5048996) (← links)
- Image Keypoint Matching Using Graph Neural Networks (Q5050354) (← links)
- (Q5053216) (← links)
- (Q5053257) (← links)
- (Q5053280) (← links)
- (Q5053287) (← links)
- (Q5053301) (← links)
- (Q5053302) (← links)
- (Q5053314) (← links)
- (Q5053316) (← links)
- (Q5053336) (← links)
- (Q5054613) (← links)
- (Q5054640) (← links)
- (Q5054669) (← links)
- (Q5054674) (← links)
- Asymptotics of representation learning in finite Bayesian neural networks* (Q5055422) (← links)
- Deep Learning--Based Dictionary Learning and Tomographic Image Reconstruction (Q5056920) (← links)
- High-Dimensional Learning Under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks (Q5060495) (← links)
- A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems (Q5065171) (← links)
- Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods (Q5066434) (← links)
- GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS (Q5067876) (← links)
- Wasserstein-Based Projections with Applications to Inverse Problems (Q5074785) (← links)
- Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization (Q5076671) (← links)
- Deep Unfitted Nitsche Method for Elliptic Interface Problems (Q5077697) (← links)
- Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
- Bayesian Brains and the Rényi Divergence (Q5081134) (← links)
- Reproducible Hyperparameter Optimization (Q5083358) (← links)
- A Diffusion Approximation Theory of Momentum Stochastic Gradient Descent in Nonconvex Optimization (Q5084492) (← links)
- Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints (Q5087714) (← links)
- Automated Reinforcement Learning (AutoRL): A Survey and Open Problems (Q5094025) (← links)
- Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent (Q5094616) (← links)
- Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior (Q5094620) (← links)
- Model-Centric Data Manifold: The Data Through the Eyes of the Model (Q5094635) (← links)
- Performance of the Low-Rank TT-SVD for Large Dense Tensors on Modern MultiCore CPUs (Q5095475) (← links)
- slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks (Q5095499) (← links)
- Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
- Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation (Q5104215) (← links)
- Frame Invariance and Scalability of Neural Operators for Partial Differential Equations (Q5106293) (← links)
- Online Mixed-Integer Optimization in Milliseconds (Q5106419) (← links)
- MultiComposite Nonconvex Optimization for Training Deep Neural Networks (Q5114402) (← links)
- Computed tomography reconstruction using deep image prior and learned reconstruction methods (Q5123693) (← links)
- PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-Efficient ReRAM (Q5126017) (← links)
