neural tangent kernel

Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks NeurIPS 2020 Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks ICML 2020 The asymptotic spectrum of the Hessian of DNN throughout training ICLR 2020 The Local Elasticity of Neural Networks ICLR 2020 The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization ICML 2020 The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks NeurIPS 2020 Towards a General Theory of Infinite-Width Limits of Neural Classifiers ICML 2020 Towards Understanding Hierarchical Learning: Benefits of Neural Representations NeurIPS 2020 Truth or backpropaganda? An empirical investigation of deep learning theory ICLR 2020 Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks NeurIPS 2020 Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective NeurIPS 2020 Approximate Inference Turns Deep Networks into Gaussian Processes NeurIPS 2019 Convergence of Adversarial Training in Overparametrized Neural Networks NeurIPS 2019 Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks NeurIPS 2019 Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels NeurIPS 2019 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers NeurIPS 2019 Limitations of Lazy Training of Two-layers Neural Network NeurIPS 2019 On Exact Computation with an Infinitely Wide Neural Net NeurIPS 2019 On the Inductive Bias of Neural Tangent Kernels NeurIPS 2019 Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel NeurIPS 2019 What Can ResNet Learn Efficiently, Going Beyond Kernels? NeurIPS 2019 Neural Tangent Kernel: Convergence and Generalization in Neural Networks NeurIPS 2018