Copertina

Variational Stochastic Gradient Descent for Deep Neural Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Haotian, Kuzina, Anna, Esmaeili, Babak, Tomczak, Jakub M
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Hierarchical VAE with a Diffusion-based VampPrior
di: Kuzina, Anna, et al.
Pubblicazione: (2024)

Stochastic Gradient Descent for Two-layer Neural Networks
di: Cao, Dinghao, et al.
Pubblicazione: (2024)

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
di: Ma, Wenquan, et al.
Pubblicazione: (2026)

KaVa: Latent Reasoning via Compressed KV-Cache Distillation
di: Kuzina, Anna, et al.
Pubblicazione: (2025)

Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks
di: Corlouer, Guillaume, et al.
Pubblicazione: (2026)

Adjacent Leader Decentralized Stochastic Gradient Descent
di: He, Haoze, et al.
Pubblicazione: (2024)

Stochastic Gradient Descent for Nonparametric Additive Regression
di: Chen, Xin, et al.
Pubblicazione: (2024)

Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
di: Peleg, Amit, et al.
Pubblicazione: (2024)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
di: Padmanabha, Govinda Anantha, et al.
Pubblicazione: (2024)

Dual Space Preconditioning for Gradient Descent in the Overparameterized Regime
di: Ghane, Reza, et al.
Pubblicazione: (2026)

Generative AI Systems: A Systems-based Perspective on Generative AI
di: Tomczak, Jakub M.
Pubblicazione: (2024)

Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
di: Kawamura, Yuya, et al.
Pubblicazione: (2024)

Stochastic Adaptive Gradient Descent Without Descent
di: Aujol, Jean-François, et al.
Pubblicazione: (2025)

On the Generalization of Stochastic Gradient Descent with Momentum
di: Ramezani-Kebrya, Ali, et al.
Pubblicazione: (2018)

Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems
di: Esmaeili, Babak, et al.
Pubblicazione: (2025)

Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
di: Liu, Weihua, et al.
Pubblicazione: (2024)

Towards Learning Stochastic Population Models by Gradient Descent
di: Kreikemeyer, Justin N., et al.
Pubblicazione: (2024)

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria
di: Tomczak, Nikodem
Pubblicazione: (2026)

A Bootstrap Perspective on Stochastic Gradient Descent
di: Lan, Hongjian, et al.
Pubblicazione: (2025)

Bolstering Stochastic Gradient Descent with Model Building
di: Birbil, S. Ilker, et al.
Pubblicazione: (2021)

Descend or Rewind? Stochastic Gradient Descent Unlearning
di: Mu, Siqiao, et al.
Pubblicazione: (2025)

Knowledge Graph-extended Retrieval Augmented Generation for Question Answering
di: Linders, Jasper, et al.
Pubblicazione: (2025)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
di: Li, Tianyou, et al.
Pubblicazione: (2023)

On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks
di: Gao, Yihang, et al.
Pubblicazione: (2024)

Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
di: Akhtiamov, Danil, et al.
Pubblicazione: (2026)

Stochastic Gradient Descent with Adaptive Data
di: Che, Ethan, et al.
Pubblicazione: (2024)

Stochastic Gradient Descent with Strategic Querying
di: Jiang, Nanfei, et al.
Pubblicazione: (2025)

Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
di: Wei, Ziyang, et al.
Pubblicazione: (2025)

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
di: Wang, Puyu, et al.
Pubblicazione: (2023)

Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
di: Hsiao, Yen-Che, et al.
Pubblicazione: (2024)

Adaptive Kernel Selection for Stein Variational Gradient Descent
di: Melcher, Moritz, et al.
Pubblicazione: (2025)

On the Theory of Continual Learning with Gradient Descent for Neural Networks
di: Taheri, Hossein, et al.
Pubblicazione: (2025)

Personalized Federated Learning with Exact Stochastic Gradient Descent
di: Nikoloutsopoulos, Sotirios, et al.
Pubblicazione: (2022)

Stochastic Gradient Descent for Gaussian Processes Done Right
di: Lin, Jihao Andreas, et al.
Pubblicazione: (2023)

Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent
di: Deng, Xiaoge, et al.
Pubblicazione: (2023)

Learning Curves of Stochastic Gradient Descent in Kernel Regression
di: Zhang, Haihan, et al.
Pubblicazione: (2025)

Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
di: Li, Jiaqi, et al.
Pubblicazione: (2025)

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
di: Della Libera, Luca, et al.
Pubblicazione: (2024)

Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descent
di: Imai, Shota, et al.
Pubblicazione: (2026)

A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization
di: Zhu, Yuchen, et al.
Pubblicazione: (2024)