Pages that link to "Item:Q2057778"

The following pages link to High-dimensional dynamics of generalization error in neural networks (Q2057778):

Displaying 34 items.

• Generalization and learning error for nonlinear perceptron. (Q1609463) (← links)
• Rademacher complexity and the generalization error of residual networks (Q2023508) (← links)
• Fast generalization error bound of deep learning without scale invariance of activation functions (Q2055056) (← links)
• Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness (Q2057701) (← links)
• A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks (Q2103975) (← links)
• Surprises in high-dimensional ridgeless least squares interpolation (Q2131262) (← links)
• Geometry and generalization: eigenvalues as predictors of where a network will fail to generalize (Q2148963) (← links)
• An analysis of training and generalization errors in shallow and deep networks (Q2185668) (← links)
• A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics (Q2197845) (← links)
• Landscape and training regimes in deep learning (Q2231925) (← links)
• Misspecified diffusion models with high-frequency observations and an application to neural networks (Q2239259) (← links)
• A statistician teaches deep learning (Q2241468) (← links)
• Free dynamics of feature learning processes (Q2679634) (← links)
• A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent* (Q5020045) (← links)
• Align, then memorise: the dynamics of learning with feedback alignment* (Q5049525) (← links)
• Align, then memorise: the dynamics of learning with feedback alignment* (Q5055410) (← links)
• Generalization error rates in kernel regression: the crossover from the noiseless to noisy regime* (Q5055412) (← links)
• An analytical theory of curriculum learning in teacher–student networks* (Q5055431) (← links)
• The inverse variance–flatness relation in stochastic gradient descent is critical for finding flat minima (Q5073270) (← links)
• (Q5148955) (← links)
• (Q5159429) (← links)
• Scaling description of generalization with number of parameters in deep learning (Q5856249) (← links)
• A Unifying Tutorial on Approximate Message Passing (Q5863992) (← links)
• A jamming transition from under- to over-parametrization affects generalization in deep learning (Q5872795) (← links)
• Prediction errors for penalized regressions based on generalized approximate message passing (Q5879248) (← links)
• Large-dimensional random matrix theory and its applications in deep learning and wireless communications (Q6063730) (← links)
• An instance-dependent simulation framework for learning with label noise (Q6174486) (← links)
• Precise learning curves and higher-order scaling limits for dot-product kernel regression (Q6611439) (← links)
• Self-consistent dynamical field theory of kernel evolution in wide neural networks (Q6611447) (← links)
• Redundant representations help generalization in wide neural networks (Q6611451) (← links)
• Tuning parameters of deep neural network training algorithms pays off: a computational study (Q6635854) (← links)
• Functional data analysis using deep neural networks (Q6642756) (← links)
• Deep networks for system identification: a survey (Q6659190) (← links)
• Dropout drops double descent (Q6670075) (← links)