Cover Image

Randomness and Interpolation Improve Gradient Descent

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Jiawen, Lefevre, Pascal, Majeed, Anwar Pp Abdul
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Halfway Escape Optimization: A Quantum-Inspired Solution for General Optimization Problems
by: Li, Jiawen, et al.
Published: (2024)

From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity
by: Liew, Yee Zhing, et al.
Published: (2026)

Trustworthiness of Stochastic Gradient Descent in Distributed Learning
by: Li, Hongyang, et al.
Published: (2024)

Gradient Descent Algorithm Survey
by: Fucheng, Deng, et al.
Published: (2025)

Noise Balance and Stationary Distribution of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2023)

ONG: Orthogonal Natural Gradient Descent
by: Yadav, Yajat, et al.
Published: (2025)

Learning Associative Memories with Gradient Descent
by: Cabannes, Vivien, et al.
Published: (2024)

Machine Learning Risk Intelligence for Green Hydrogen Investment: Insights for Duqm R3 Auction
by: Nwafor, Obumneme, et al.
Published: (2025)

Elastic Multi-Gradient Descent for Parallel Continual Learning
by: Lyu, Fan, et al.
Published: (2024)

Adaptive Heavy-Tailed Stochastic Gradient Descent
by: Gong, Bodu, et al.
Published: (2025)

Vanilla Gradient Descent for Oblique Decision Trees
by: Panda, Subrat Prasad, et al.
Published: (2024)

Stochastic Gradient Descent with Momentum is Algorithmically Stable
by: Lei, Yunwen, et al.
Published: (2026)

Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians
by: Engelken, Rainer
Published: (2023)

The Initialization Determines Whether In-Context Learning Is Gradient Descent
by: Xie, Shifeng, et al.
Published: (2025)

Revisiting the Initial Steps in Adaptive Gradient Descent Optimization
by: Abuduweili, Abulikemu, et al.
Published: (2024)

Efficient Search for Customized Activation Functions with Gradient Descent
by: Strack, Lukas, et al.
Published: (2024)

Conflict-Averse Gradient Descent for Multi-task Learning
by: Liu, Bo, et al.
Published: (2021)

Can LLMs predict the convergence of Stochastic Gradient Descent?
by: Zekri, Oussama, et al.
Published: (2024)

Gradient Descent Efficiency Index
by: Dhingra, Aviral
Published: (2024)

Fisher-Orthogonal Projected Natural Gradient Descent for Continual Learning
by: Garg, Ishir, et al.
Published: (2026)

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

On Double Descent in Reinforcement Learning with LSTD and Random Features
by: Brellmann, David, et al.
Published: (2023)

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
by: Huang, Jianhao, et al.
Published: (2025)

Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent
by: Kumar, Mohit, et al.
Published: (2024)

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
by: Marton, Sascha, et al.
Published: (2023)

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
by: Kumar, Ramnath, et al.
Published: (2023)

Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent
by: Chen, Bo, et al.
Published: (2024)

Improved Crop and Weed Detection with Diverse Data Ensemble Learning
by: Asad, Muhammad Hamza, et al.
Published: (2023)

Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training
by: Ma, Yuhan, et al.
Published: (2024)

PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
by: Xu, Mingjing, et al.
Published: (2024)

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
by: Wang, Puyu, et al.
Published: (2026)

Geodesic Gradient Descent: A Generic and Learning-rate-free Optimizer on Objective Function-induced Manifolds
by: Hu, Liwei, et al.
Published: (2026)

Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization
by: Liang, Chen, et al.
Published: (2026)

Artificial Intelligence for Green Hydrogen Yield Prediction and Site Suitability using SHAP-Based Composite Index: Focus on Oman
by: Nwafor, Obumneme Zimuzor, et al.
Published: (2025)

Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training
by: Lu, Yishun, et al.
Published: (2025)

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

Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch Gradient Descent
by: Yang, Haoyu, et al.
Published: (2024)

Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
by: Gatmiry, Khashayar, et al.
Published: (2024)

Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
by: Hennick, Max, et al.
Published: (2025)