The following pages link to PyTorch (Q32752):
Displaying 50 items.
- Data science applications to string theory (Q2187812) (← links)
- End-to-end learning of decision trees and forests (Q2193582) (← links)
- Deep neural network approach to forward-inverse problems (Q2197226) (← links)
- Anomaly detection with inexact labels (Q2203336) (← links)
- Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio (Q2205170) (← links)
- ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (Q2212518) (← links)
- Data-driven discovery of PDEs in complex datasets (Q2214651) (← links)
- Embedding-based silhouette community detection (Q2217405) (← links)
- Spanning attack: reinforce black-box attacks with unlabeled data (Q2217425) (← links)
- Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication (Q2217433) (← links)
- Signed particles and neural networks, towards efficient simulations of quantum systems (Q2220562) (← links)
- Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972) (← links)
- Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
- A look at robustness and stability of \(\ell_1\)-versus \(\ell_0\)-regularization: discussion of papers by Bertsimas et al. and Hastie et al. (Q2225318) (← links)
- Search for the global extremum using the correlation indicator for neural networks supervised learning (Q2226965) (← links)
- Mini-workshop: Computational optimization on manifolds. Abstracts from the mini-workshop held November 15--21, 2020 (online meeting) (Q2232319) (← links)
- SyReNN: a tool for analyzing deep neural networks (Q2233513) (← links)
- Data-driven identification of 2D partial differential equations using extracted physical features (Q2236988) (← links)
- Spatiotemporal adaptive neural network for long-term forecasting of financial time series (Q2237157) (← links)
- A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330) (← links)
- Learning nonlocal constitutive models with neural networks (Q2237430) (← links)
- An FE-DMN method for the multiscale analysis of short fiber reinforced plastic components (Q2237450) (← links)
- Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← links)
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
- K-plex cover pooling for graph neural networks (Q2238356) (← links)
- Counterfactual state explanations for reinforcement learning agents via generative deep learning (Q2238641) (← links)
- Neural probabilistic logic programming in DeepProbLog (Q2238688) (← links)
- Simple feature pyramid network for weakly supervised object localization using multi-scale information (Q2239134) (← links)
- A framework for quantum-classical cryptographic translation (Q2239408) (← links)
- A statistician teaches deep learning (Q2241468) (← links)
- Self-attention implicit function networks for 3D dental data completion (Q2243182) (← links)
- NPLIC: a machine learning approach to piecewise linear interface construction (Q2245365) (← links)
- Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- Stochastic subgradient method converges on tame functions (Q2291732) (← links)
- Black-box learning of multigrid parameters (Q2291999) (← links)
- A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
- Wasserstein index generation model: automatic generation of time-series index with application to economic policy uncertainty (Q2292829) (← links)
- A survey on semi-supervised learning (Q2303675) (← links)
- Second-order networks in Pytorch (Q2305295) (← links)
- FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378) (← links)
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications (Q2310233) (← links)
- From receptive profiles to a metric model of V1 (Q2315385) (← links)
- Blended coarse gradient descent for full quantization of deep neural networks (Q2319868) (← links)
- A deep-learning-based geological parameterization for history matching complex models (Q2323494) (← links)
- GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model (Q2329768) (← links)
- Representation of surfaces with normal cycles and application to surface registration (Q2331083) (← links)
- Convolutional neural network models of V1 responses to complex patterns (Q2418231) (← links)
- An exploratory study on machine learning to couple numerical solutions of partial differential equations (Q2656809) (← links)
