Epoch-Wise Double Descent Triggered by Learning a Single Sample
暂无分享,去创建一个
[1] Yoshua Bengio,et al. Multi-scale Feature Learning Dynamics: Insights for Double Descent , 2021, ICML.
[2] Erkun Yang,et al. Understanding and Improving Early Stopping for Learning with Noisy Labels , 2021, NeurIPS.
[3] Hanlin Tang,et al. On the geometry of generalization and memorization in deep neural networks , 2021, ICLR.
[4] Samy Bengio,et al. Understanding deep learning (still) requires rethinking generalization , 2021, Commun. ACM.
[5] Jeffrey Pennington,et al. Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition , 2020, NeurIPS.
[6] Kan Chen,et al. A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks , 2020, AISTATS.
[7] Reinhard Heckel,et al. Early Stopping in Deep Networks: Double Descent and How to Eliminate it , 2020, ICLR.
[8] Jeffrey Pennington,et al. The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization , 2020, ICML.
[9] Sheng Liu,et al. Early-Learning Regularization Prevents Memorization of Noisy Labels , 2020, NeurIPS.
[10] Levent Sagun,et al. Triple descent and the two kinds of overfitting: where and why do they appear? , 2020, NeurIPS.
[11] G. Biroli,et al. Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime , 2020, ICML.
[12] Boaz Barak,et al. Deep double descent: where bigger models and more data hurt , 2019, ICLR.
[13] Andrea Montanari,et al. The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve , 2019, Communications on Pure and Applied Mathematics.
[14] Samet Oymak,et al. Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks , 2019, AISTATS.
[15] Jaehoon Lee,et al. Wide neural networks of any depth evolve as linear models under gradient descent , 2019, NeurIPS.
[16] Samy Bengio,et al. Identity Crisis: Memorization and Generalization under Extreme Overparameterization , 2019, ICLR.
[17] Ruosong Wang,et al. Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks , 2019, ICML.
[18] Mikhail Belkin,et al. Reconciling modern machine-learning practice and the classical bias–variance trade-off , 2018, Proceedings of the National Academy of Sciences.
[19] Samet Oymak,et al. Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? , 2018, ICML.
[20] Liwei Wang,et al. Gradient Descent Finds Global Minima of Deep Neural Networks , 2018, ICML.
[21] Yuanzhi Li,et al. A Convergence Theory for Deep Learning via Over-Parameterization , 2018, ICML.
[22] Arthur Jacot,et al. Neural Tangent Kernel: Convergence and Generalization in Neural Networks , 2018, NeurIPS.
[23] Yann LeCun,et al. Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks , 2018, ArXiv.
[24] Mikhail Belkin,et al. To understand deep learning we need to understand kernel learning , 2018, ICML.
[25] Andrew M. Saxe,et al. High-dimensional dynamics of generalization error in neural networks , 2017, Neural Networks.
[26] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[27] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[28] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[29] John C. Duchi,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011 .