Image de couverture de livre

ONG: Orthogonal Natural Gradient Descent

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Yadav, Yajat, Mendoza, Patrick, Korrapati, Jathin
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

VROOM - Visual Reconstruction over Onboard Multiview
par: Yadav, Yajat, et autres
Publié: (2025)

Fisher-Orthogonal Projected Natural Gradient Descent for Continual Learning
par: Garg, Ishir, et autres
Publié: (2026)

Can Transformers Break Encryption Schemes via In-Context Learning?
par: Korrapati, Jathin, et autres
Publié: (2025)

Discrete vs. Continuous Trade-offs for Generative Models
par: Korrapati, Jathin, et autres
Publié: (2024)

Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training
par: Lu, Yishun, et autres
Publié: (2025)

Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
par: Liu, Weihua, et autres
Publié: (2024)

Gradient Descent Algorithm Survey
par: Fucheng, Deng, et autres
Publié: (2025)

Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
par: Wang, Shaoqi, et autres
Publié: (2024)

Natural Gradient Descent for Online Continual Learning
par: Khawand, Joe, et autres
Publié: (2026)

Randomness and Interpolation Improve Gradient Descent
par: Li, Jiawen, et autres
Publié: (2025)

Learning Associative Memories with Gradient Descent
par: Cabannes, Vivien, et autres
Publié: (2024)

Adaptive Heavy-Tailed Stochastic Gradient Descent
par: Gong, Bodu, et autres
Publié: (2025)

Vanilla Gradient Descent for Oblique Decision Trees
par: Panda, Subrat Prasad, et autres
Publié: (2024)

Stochastic Gradient Descent with Momentum is Algorithmically Stable
par: Lei, Yunwen, et autres
Publié: (2026)

Generalized Euler Logarithm and its Applications in Machine Learning: Natural Gradient, Backpropagation, Generalized EG, Mirror Descent and OLPS
par: Cichocki, Andrzej
Publié: (2025)

The Initialization Determines Whether In-Context Learning Is Gradient Descent
par: Xie, Shifeng, et autres
Publié: (2025)

Elastic Multi-Gradient Descent for Parallel Continual Learning
par: Lyu, Fan, et autres
Publié: (2024)

Revisiting the Initial Steps in Adaptive Gradient Descent Optimization
par: Abuduweili, Abulikemu, et autres
Publié: (2024)

Efficient Search for Customized Activation Functions with Gradient Descent
par: Strack, Lukas, et autres
Publié: (2024)

Conflict-Averse Gradient Descent for Multi-task Learning
par: Liu, Bo, et autres
Publié: (2021)

Noise Balance and Stationary Distribution of Stochastic Gradient Descent
par: Ziyin, Liu, et autres
Publié: (2023)

Can LLMs predict the convergence of Stochastic Gradient Descent?
par: Zekri, Oussama, et autres
Publié: (2024)

Gradient Descent Efficiency Index
par: Dhingra, Aviral
Publié: (2024)

Gradient Regularized Natural Gradients
par: Dash, Satya Prakash, et autres
Publié: (2026)

Training Data Selection with Gradient Orthogonality for Efficient Domain Adaptation
par: Zhang, Xiyang, et autres
Publié: (2026)

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
par: Huang, Jianhao, et autres
Publié: (2025)

Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent
par: Kumar, Mohit, et autres
Publié: (2024)

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
par: Marton, Sascha, et autres
Publié: (2023)

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
par: Kumar, Ramnath, et autres
Publié: (2023)

Provable Benefit of Sign Descent: A Minimal Model Under Heavy-Tailed Class Imbalance
par: Yadav, Robin, et autres
Publié: (2025)

PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
par: Xu, Mingjing, et autres
Publié: (2024)

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
par: Wang, Puyu, et autres
Publié: (2026)

Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization
par: Liang, Chen, et autres
Publié: (2026)

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
par: Liu, Junkang, et autres
Publié: (2026)

Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization
par: Kaplan, Ahmet
Publié: (2026)

Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch Gradient Descent
par: Yang, Haoyu, et autres
Publié: (2024)

Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
par: Gatmiry, Khashayar, et autres
Publié: (2024)

Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
par: Hennick, Max, et autres
Publié: (2025)

Finite-Time Analysis of Gradient Descent for Shallow Transformers
par: Arda, Enes, et autres
Publié: (2026)

On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks
par: Gao, Yihang, et autres
Publié: (2024)