The following pages link to Keras (Q27379):
Displaying 50 items.
- Discriminatively learned hierarchical rank pooling networks (Q2193883) (← links)
- Soft sensor modeling of key effluent parameters in wastewater treatment process based on SAE-NN (Q2199925) (← 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)
- Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network (Q2219521) (← links)
- Solving electrical impedance tomography with deep learning (Q2223016) (← links)
- Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
- Deep multiscale model learning (Q2223279) (← links)
- Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning (Q2225349) (← links)
- Recent developments combining ensemble smoother and deep generative networks for facies history matching (Q2225361) (← links)
- Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media (Q2226818) (← links)
- SyReNN: a tool for analyzing deep neural networks (Q2233513) (← links)
- Machine learning Lie structures \& applications to physics (Q2233703) (← links)
- A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM (Q2237268) (← links)
- Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy (Q2237307) (← links)
- Machine learning augmented reduced-order models for FFR-prediction (Q2237421) (← 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)
- Revealing pairs-trading opportunities with long short-term memory networks (Q2239926) (← links)
- On ensembles, I-optimality, and active learning (Q2241706) (← links)
- Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data (Q2246386) (← links)
- Neural networks for topology optimization (Q2274318) (← links)
- Plus-minus player ratings for soccer (Q2286991) (← links)
- Multilevel and multiscale feature aggregation in deep networks for facial constitution classification (Q2299895) (← links)
- Improving attacks on round-reduced Speck32/64 using deep learning (Q2304981) (← links)
- \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (Q2309352) (← links)
- A multiscale neural network based on hierarchical nested bases (Q2319969) (← links)
- Joint detection of malicious domains and infected clients (Q2320564) (← links)
- A deep learning framework for hybrid heterogeneous transfer learning (Q2321331) (← links)
- The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling (Q2333058) (← links)
- Deep learning assisted heuristic tree search for the container pre-marshalling problem (Q2333140) (← links)
- Hierarchical clustering with deep q-learning (Q2414694) (← links)
- Deep learning of biological models from data: applications to ODE models (Q2659803) (← links)
- Learning nonlinear state-space models using autoencoders (Q2665158) (← links)
- Energy consumption prediction model with deep inception residual network inspiration and LSTM (Q2666225) (← links)
- A probabilistic approximate logic for neuro-symbolic learning and reasoning (Q2667187) (← links)
- Learning the mapping \(\mathbf{x}\mapsto \sum\limits_{i=1}^d x_i^2\): the cost of finding the needle in a haystack (Q2667355) (← links)
- A non-intrusive correction algorithm for classification problems with corrupted data (Q2667356) (← links)
- Efficient and sparse neural networks by pruning weights in a multiobjective learning approach (Q2669736) (← links)
- Machine learning constitutive models of elastomeric foams (Q2670325) (← links)
- A hybrid inference system for improved curvature estimation in the level-set method using machine learning (Q2671404) (← links)
- Surrogate convolutional neural network models for steady computational fluid dynamics simulations (Q2672202) (← links)
- Error-correcting neural networks for two-dimensional curvature computation in the level-set method (Q2674272) (← links)
- Machine learning applied to asteroid dynamics (Q2675566) (← links)
- Bridging deep convolutional autoencoders and ensemble smoothers for improved estimation of channelized reservoirs (Q2676511) (← links)
- Efficient use of data for LSTM mortality forecasting (Q2677941) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Learning algebraic models of quantum entanglement (Q2681640) (← links)
- Optimization of artificial neural networks models applied to the identification of images of asteroids' resonant arguments (Q2685201) (← links)
- Deep quantile and deep composite triplet regression (Q2685516) (← links)
