Copertina

Gauss-Newton Natural Gradient Descent for Shape Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	King, James, Berzins, Arturs, Mishra, Siddhartha, Zeinhofer, Marius
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics
di: Jnini, Anas, et al.
Pubblicazione: (2024)

Incremental Gauss-Newton Descent for Machine Learning
di: Korbit, Mikalai, et al.
Pubblicazione: (2024)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
di: Orvieto, Antonio, et al.
Pubblicazione: (2024)

Error whitening: Why Gauss-Newton outperforms Newton
di: McKay, Maricela Best, et al.
Pubblicazione: (2026)

Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
di: Chiang, John
Pubblicazione: (2022)

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
di: Mishra, Neel, et al.
Pubblicazione: (2024)

Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
di: Ke, Zhifa, et al.
Pubblicazione: (2023)

Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence
di: Datar, Adwait, et al.
Pubblicazione: (2025)

Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks
di: Adeoye, Adeyemi D., et al.
Pubblicazione: (2024)

Incremental Gauss--Newton Methods with Superlinear Convergence Rates
di: Zhou, Zhiling, et al.
Pubblicazione: (2024)

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

Learning Provably Improves the Convergence of Gradient Descent
di: Song, Qingyu, et al.
Pubblicazione: (2025)

Enhancing Fractional Gradient Descent with Learned Optimizers
di: Sobotka, Jan, et al.
Pubblicazione: (2025)

Functional Neural Wavefunction Optimization
di: Armegioiu, Victor, et al.
Pubblicazione: (2025)

Corner Gradient Descent
di: Yarotsky, Dmitry
Pubblicazione: (2025)

Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
di: Jnini, Anas, et al.
Pubblicazione: (2025)

On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
di: Qin, Zhen, et al.
Pubblicazione: (2025)

Adaptive Conditional Gradient Descent
di: Khademi, Abbas, et al.
Pubblicazione: (2025)

$k$-SVD with Gradient Descent
di: Jedra, Yassir, et al.
Pubblicazione: (2025)

Anytime Acceleration of Gradient Descent
di: Zhang, Zihan, et al.
Pubblicazione: (2024)

Non-Asymptotic Optimization and Generalization Bounds for Stochastic Gauss-Newton in Overparameterized Models
di: Cayci, Semih
Pubblicazione: (2025)

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
di: Fang, Cheng, et al.
Pubblicazione: (2025)

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)

Unraveling the Gradient Descent Dynamics of Transformers
di: Song, Bingqing, et al.
Pubblicazione: (2024)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
di: Bonet, Clément, et al.
Pubblicazione: (2024)

Derivatives of Stochastic Gradient Descent in parametric optimization
di: Iutzeler, Franck, et al.
Pubblicazione: (2024)

Convergence of Alternating Gradient Descent for Matrix Factorization
di: Ward, Rachel, et al.
Pubblicazione: (2023)

On Penalty-based Bilevel Gradient Descent Method
di: Shen, Han, et al.
Pubblicazione: (2023)

A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
di: Xu, Ziqing, et al.
Pubblicazione: (2025)

Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
di: Veprikov, Andrey, et al.
Pubblicazione: (2025)

Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent
di: Umeda, Hikaru, et al.
Pubblicazione: (2024)

Position: Optimization in SciML Should Employ the Function Space Geometry
di: Müller, Johannes, et al.
Pubblicazione: (2024)

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
di: Kunstner, Frederik, et al.
Pubblicazione: (2025)

Gradient Regularized Newton Boosting Trees with Global Convergence
di: Zozoulenko, Nikita, et al.
Pubblicazione: (2026)

Gradient Descent's Last Iterate is Often (slightly) Suboptimal
di: Kornowski, Guy, et al.
Pubblicazione: (2026)

Non-Euclidean Gradient Descent Operates at the Edge of Stability
di: Islamov, Rustem, et al.
Pubblicazione: (2026)

Functional Central Limit Theorem for Stochastic Gradient Descent
di: Flamand, Kessang, et al.
Pubblicazione: (2026)

AutoGD: Automatic Learning Rate Selection for Gradient Descent
di: Surjanovic, Nikola, et al.
Pubblicazione: (2025)

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