Copertina

Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Safder, Adeel
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Statistics Theory
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
di: Zhang, Huiqi, et al.
Pubblicazione: (2025)

Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond
di: Sawaya, Kazuma
Pubblicazione: (2025)

Dropout Drops Double Descent
di: Yang, Tian-Le, et al.
Pubblicazione: (2023)

Inference for Deep Neural Network Estimators in Generalized Nonparametric Models
di: Meng, Xuran, et al.
Pubblicazione: (2025)

On the Natural Gradient of the Evidence Lower Bound
di: Ay, Nihat, et al.
Pubblicazione: (2023)

Pointwise Generalization in Deep Neural Networks
di: Li, Shaojie, et al.
Pubblicazione: (2026)

Upper Bounds for Local Learning Coefficients of Three-Layer Neural Networks
di: Kurumadani, Yuki
Pubblicazione: (2026)

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
di: Li, Gen, et al.
Pubblicazione: (2025)

Dropout Regularization Versus $\ell_2$-Penalization in the Linear Model
di: Clara, Gabriel, et al.
Pubblicazione: (2023)

Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping
di: Yang, Yingzhen, et al.
Pubblicazione: (2024)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
di: Altekrüger, Fabian, et al.
Pubblicazione: (2023)

Exploring the Complexity of Deep Neural Networks through Functional Equivalence
di: Shen, Guohao
Pubblicazione: (2023)

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
di: Frei, Spencer, et al.
Pubblicazione: (2022)

Feature Incremental Clustering with Generalization Bounds
di: Zhang, Jing, et al.
Pubblicazione: (2026)

Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate
di: Yang, Yingzhen, et al.
Pubblicazione: (2026)

Comparing Comparators in Generalization Bounds
di: Hellström, Fredrik, et al.
Pubblicazione: (2023)

Tight Generalization Bounds for Large-Margin Halfspaces
di: Larsen, Kasper Green, et al.
Pubblicazione: (2025)

PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime
di: Masegosa, Andrés R., et al.
Pubblicazione: (2023)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
di: Balasubramanian, Krishnakumar, et al.
Pubblicazione: (2025)

Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks
di: Kim, Sehwan, et al.
Pubblicazione: (2025)

Generating Rectifiable Measures through Neural Networks
di: Riegler, Erwin, et al.
Pubblicazione: (2024)

Bridging Theory and Practice: A Stochastic Learning-Optimization Model for Resilient Automotive Supply Chains
di: Shahnawaz, Muhammad, et al.
Pubblicazione: (2025)

Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
di: Cheng, Ziheng, et al.
Pubblicazione: (2025)

Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
di: Ghosh, Soumyadip, et al.
Pubblicazione: (2021)

Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
di: Li, Gen, et al.
Pubblicazione: (2025)

Approximation of RKHS Functionals by Neural Networks
di: Zhou, Tian-Yi, et al.
Pubblicazione: (2024)

Understanding Learning Invariance in Deep Linear Networks
di: Duan, Hao, et al.
Pubblicazione: (2025)

Characteristic Learning for Provable One Step Generation
di: Ding, Zhao, et al.
Pubblicazione: (2024)

Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees
di: Chatterjee, Sabyasachi, et al.
Pubblicazione: (2019)

Simultaneous Approximation of the Score Function and Its Derivatives by Deep Neural Networks
di: Yakovlev, Konstantin, et al.
Pubblicazione: (2025)

Consistency for Large Neural Networks: Regression and Classification
di: Zhan, Haoran, et al.
Pubblicazione: (2024)

Phase Transitions for Feature Learning in Neural Networks
di: Montanari, Andrea, et al.
Pubblicazione: (2026)

Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets
di: Larsen, Kasper Green, et al.
Pubblicazione: (2025)

General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions
di: Li, Yicheng
Pubblicazione: (2026)

Neural Networks Generalize on Low Complexity Data
di: Chatterjee, Sourav, et al.
Pubblicazione: (2024)

Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories
di: Zhang, Haobo, et al.
Pubblicazione: (2024)

Deep Distributional Learning with Non-crossing Quantile Network
di: Shen, Guohao, et al.
Pubblicazione: (2025)

Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
di: Liu, Xingchi, et al.
Pubblicazione: (2024)

Cross-regularization: Adaptive Model Complexity through Validation Gradients
di: Brito, Carlos Stein
Pubblicazione: (2025)

On the Provable Performance Guarantee of Efficient Reasoning Models
di: Zeng, Hao, et al.
Pubblicazione: (2025)