Pages that link to "Item:Q5361282"

The following pages link to Breaking the Curse of Dimensionality with Convex Neural Networks (Q5361282):

Displaying 50 items.

• Machine learning from a continuous viewpoint. I (Q829085) (← links)
• Analysis of a two-layer neural network via displacement convexity (Q1996787) (← links)
• Ensemble feature selection using election methods and ranker clustering (Q2004741) (← links)
• Topological properties of the set of functions generated by neural networks of fixed size (Q2031060) (← links)
• Linearized two-layers neural networks in high dimension (Q2039801) (← links)
• High-dimensional index volatility models via Stein's identity (Q2040038) (← links)
• Error bounds for deep ReLU networks using the Kolmogorov-Arnold superposition theorem (Q2055036) (← links)
• Fast generalization error bound of deep learning without scale invariance of activation functions (Q2055056) (← links)
• Optimally weighted loss functions for solving PDEs with neural networks (Q2068635) (← links)
• Fitting small piece-wise linear neural network models to interpolate data sets (Q2072583) (← links)
• Supervised learning from noisy observations: combining machine-learning techniques with data assimilation (Q2077682) (← links)
• CAS4DL: Christoffel adaptive sampling for function approximation via deep learning (Q2098302) (← links)
• Understanding neural networks with reproducing kernel Banach spaces (Q2105111) (← links)
• The interpolation phase transition in neural networks: memorization and generalization under lazy training (Q2105197) (← links)
• Representation formulas and pointwise properties for Barron functions (Q2113295) (← links)
• The Barron space and the flow-induced function spaces for neural network models (Q2117337) (← links)
• Robust and resource-efficient identification of two hidden layer neural networks (Q2117339) (← links)
• High-order approximation rates for shallow neural networks with cosine and \(\mathrm{ReLU}^k\) activation functions (Q2118396) (← links)
• Challenges in optimization with complex PDE-systems. Abstracts from the workshop held February 14--20, 2021 (hybrid meeting) (Q2131202) (← links)
• Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q2136443) (← links)
• Degrees of freedom for off-the-grid sparse estimation (Q2137058) (← links)
• A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions (Q2145074) (← links)
• A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers (Q2148995) (← links)
• Sparse optimization on measures with over-parameterized gradient descent (Q2149558) (← links)
• Deep learning for constrained utility maximisation (Q2152236) (← links)
• Approximation properties of deep ReLU CNNs (Q2157922) (← links)
• Uniform approximation rates and metric entropy of shallow neural networks (Q2157931) (← links)
• Nonconvex regularization for sparse neural networks (Q2168678) (← links)
• Function approximation by deep networks (Q2191837) (← links)
• Nonparametric regression using deep neural networks with ReLU activation function (Q2215715) (← links)
• Kolmogorov width decay and poor approximators in machine learning: shallow neural networks, random feature models and neural tangent kernels (Q2226529) (← links)
• Landscape and training regimes in deep learning (Q2231925) (← 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)
• Conditional regression for single-index models (Q2676954) (← links)
• The geometry of off-the-grid compressed sensing (Q2684465) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
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• Multikernel Regression with Sparsity Constraint (Q4999353) (← links)
• The Gap between Theory and Practice in Function Approximation with Deep Neural Networks (Q4999396) (← links)
• On the Effectiveness of Richardson Extrapolation in Data Science (Q5018900) (← links)
• When do neural networks outperform kernel methods?* (Q5020050) (← links)
• Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (Q5021399) (← links)
• (Q5053206) (← links)
• Two-Layer Neural Networks with Values in a Banach Space (Q5055293) (← links)
• Particle dual averaging: optimization of mean field neural network with global convergence rate analysis* (Q5055425) (← links)
• Locality defeats the curse of dimensionality in convolutional teacher–student scenarios* (Q5055428) (← links)