Pages that link to "Item:Q2961936"

The following pages link to Human-level concept learning through probabilistic program induction (Q2961936):

Displaying 50 items.

• Three-way concept learning based on cognitive operators: an information fusion viewpoint (Q518620) (← links)
• Deep learning algorithm with visual impression (Q1751402) (← links)
• Few-shot learning based on hierarchical classification via multi-granularity relation networks (Q2077003) (← links)
• GLRM: logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy (Q2077014) (← links)
• The role of bilinguals in the Bayesian naming game (Q2077817) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)
• Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning (Q2093367) (← links)
• Prediction of the number of solitons for initial value of nonlinear Schrödinger equation based on the deep learning method (Q2107244) (← links)
• The data-driven localized wave solutions of the derivative nonlinear Schrödinger equation by using improved PINN approach (Q2124077) (← links)
• Splicing learning: a novel few-shot learning approach (Q2126264) (← links)
• Multiway \(p\)-spectral graph cuts on Grassmann manifolds (Q2127266) (← links)
• Calibrating generative models: the probabilistic Chomsky-Schützenberger hierarchy (Q2177476) (← links)
• Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach (Q2183600) (← links)
• Deep learning for generic object detection: a survey (Q2193851) (← links)
• Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
• Making sense of raw input (Q2238697) (← links)
• Kandinsky patterns (Q2238716) (← links)
• Few-shot learning in deep networks through global prototyping (Q2292229) (← links)
• Neural network as a function approximator and its application in solving differential equations (Q2313205) (← links)
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336) (← links)
• The teaching size: computable teachers and learners for universal languages (Q2320594) (← links)
• Computing programs for generalized planning using a classical planner (Q2321292) (← links)
• Toward any-language zero-shot topic classification of textual documents (Q2321313) (← links)
• Online learning of symbolic concepts (Q2403016) (← links)
• An exact penalty approach for optimization with nonnegative orthogonality constraints (Q2687065) (← links)
• How to Grow a Mind: Statistics, Structure, and Abstraction (Q3101800) (← links)
• Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses (Q3303061) (← links)
• (Q4969129) (← links)
• Predicting the Ease of Human Category Learning Using Radial Basis Function Networks (Q5004299) (← links)
• JAX, M.D. A framework for differentiable physics* (Q5020064) (← links)
• Bias-Free Yuragi Learning (Q5049729) (← links)
• AI, visual imagery, and a case study on the challenges posed by human intelligence tests (Q5073202) (← links)
• A bird’s-eye view of naming game dynamics: From trait competition to Bayesian inference (Q5119453) (← links)
• Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems (Q5119465) (← links)
• Active Inference, Belief Propagation, and the Bethe Approximation (Q5157239) (← links)
• Morpho-MNIST: quantitative assessment and diagnostics for representation learning (Q5214288) (← links)
• Heuristic search of optimal machine teaching curricula (Q6053815) (← links)
• JAX-fluids: a fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows (Q6097327) (← links)
• Data-driven vortex solitons and parameter discovery of 2D generalized nonlinear Schrödinger equations with a \(\mathcal{PT}\)-symmetric optical lattice (Q6103701) (← links)
• Large scale multi-output multi-class classification using Gaussian processes (Q6106450) (← links)
• Notes on the improvement of concept-cognitive learning accuracy (Q6114032) (← links)
• How to describe the spatial near-far relations among concepts? (Q6114034) (← links)
• Hierarchical quotient space-based concept cognition for knowledge graphs (Q6118902) (← links)
• Number of solitons emerged in the initial profile of shallow water using convolutional neural networks (Q6130982) (← links)
• Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
• Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
• Towards well-generalizing meta-learning via adversarial task augmentation (Q6157204) (← links)
• Are LSTMs good few-shot learners? (Q6188063) (← links)