The following pages link to PyTorch (Q32752):
Displaying 50 items.
- NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems (Q2700171) (← links)
- TeNeS: tensor network solver for quantum lattice systems (Q2700750) (← links)
- Deep Variational Inference (Q3300544) (← links)
- Back-Propagation Through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods (Q3381991) (← links)
- The loss surfaces of neural networks with general activation functions (Q3382363) (← links)
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (Q4558167) (← links)
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- Approximate supervised learning of quantum gates via ancillary qubits (Q4620290) (← links)
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- A robust multi-batch L-BFGS method for machine learning (Q4972551) (← links)
- A Simple and Efficient Tensor Calculus for Machine Learning (Q4988921) (← links)
- Index tracking through deep latent representation learning (Q4991048) (← links)
- A Strong Law of Large Numbers for Scrambled Net Integration (Q4992613) (← links)
- Deep Learning in Computational Mechanics (Q4995007) (← links)
- Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (Q4997904) (← links)
- Deep Learning with Python (Q4998419) (← links)
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- Artificial Neural Network as a Universal Model of Nonlinear Dynamical Systems (Q4999680) (← links)
- Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM (Q5000571) (← links)
- Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences (Q5001377) (← links)
- The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks (Q5004332) (← links)
- Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks (Q5004342) (← links)
- Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses (Q5004367) (← links)
- Equivariant neural networks for inverse problems (Q5006365) (← links)
- The Structure of Conservative Gradient Fields (Q5010051) (← links)
- Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning (Q5014842) (← links)
- Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks (Q5015302) (← links)
- Predicting drag on rough surfaces by transfer learning of empirical correlations (Q5019243) (← links)
- Categorical semantics of a simple differential programming language (Q5019684) (← links)
- Deep double descent: where bigger models and more data hurt* (Q5020041) (← links)
- Reconstruction of pairwise interactions using energy-based models* (Q5020047) (← links)
- Path integral based convolution and pooling for graph neural networks* (Q5020053) (← links)
- Augmenting physical models with deep networks for complex dynamics forecasting* (Q5020055) (← links)
- Entropic gradient descent algorithms and wide flat minima* (Q5020063) (← links)
- JAX, M.D. A framework for differentiable physics* (Q5020064) (← links)
- PETSc TSAdjoint: A Discrete Adjoint ODE Solver for First-Order and Second-Order Sensitivity Analysis (Q5022486) (← links)
- High generalization performance structured self-attention model for knapsack problem (Q5025155) (← links)
