Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
Fuente:
arXiv
Guardado en:
| Autores principales: | Hsiao, Yen-Che, Dutta, Abhishek |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence
por: Hsiao, Yen-Che, et al.
Publicado: (2024)
por: Hsiao, Yen-Che, et al.
Publicado: (2024)
On the Theory of Continual Learning with Gradient Descent for Neural Networks
por: Taheri, Hossein, et al.
Publicado: (2025)
por: Taheri, Hossein, et al.
Publicado: (2025)
Efficient Search for Customized Activation Functions with Gradient Descent
por: Strack, Lukas, et al.
Publicado: (2024)
por: Strack, Lukas, et al.
Publicado: (2024)
Stochastic Gradient Descent with Adaptive Data
por: Che, Ethan, et al.
Publicado: (2024)
por: Che, Ethan, et al.
Publicado: (2024)
Stochastic Gradient Descent for Two-layer Neural Networks
por: Cao, Dinghao, et al.
Publicado: (2024)
por: Cao, Dinghao, et al.
Publicado: (2024)
Variational Stochastic Gradient Descent for Deep Neural Networks
por: Chen, Haotian, et al.
Publicado: (2024)
por: Chen, Haotian, et al.
Publicado: (2024)
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
por: Liu, Junkang, et al.
Publicado: (2026)
por: Liu, Junkang, et al.
Publicado: (2026)
Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
por: Ma, Wenquan, et al.
Publicado: (2026)
por: Ma, Wenquan, et al.
Publicado: (2026)
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
por: Wang, Puyu, et al.
Publicado: (2023)
por: Wang, Puyu, et al.
Publicado: (2023)
Coordinate Descent for Network Linearization
por: Rakhlin, Vlad, et al.
Publicado: (2025)
por: Rakhlin, Vlad, et al.
Publicado: (2025)
Block Coordinate Descent for Neural Networks Provably Finds Global Minima
por: Akiyama, Shunta
Publicado: (2025)
por: Akiyama, Shunta
Publicado: (2025)
Stochastic Adaptive Gradient Descent Without Descent
por: Aujol, Jean-François, et al.
Publicado: (2025)
por: Aujol, Jean-François, et al.
Publicado: (2025)
Occam Gradient Descent
por: Kausik, B. N.
Publicado: (2024)
por: Kausik, B. N.
Publicado: (2024)
Distributed Gradient Descent for Functional Learning
por: Yu, Zhan, et al.
Publicado: (2023)
por: Yu, Zhan, et al.
Publicado: (2023)
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
por: Zhang, Chenyang, et al.
Publicado: (2025)
por: Zhang, Chenyang, et al.
Publicado: (2025)
Approximation and Gradient Descent Training with Neural Networks
por: Welper, G.
Publicado: (2024)
por: Welper, G.
Publicado: (2024)
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
por: Peleg, Amit, et al.
Publicado: (2024)
por: Peleg, Amit, et al.
Publicado: (2024)
Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
por: Buskulic, Nathan, et al.
Publicado: (2024)
por: Buskulic, Nathan, et al.
Publicado: (2024)
Step by Step: Adaptive Gradient Descent for Training L-Lipschitz Neural Networks
por: Sung, Kyle, et al.
Publicado: (2025)
por: Sung, Kyle, et al.
Publicado: (2025)
Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
por: Alexander, Yotam, et al.
Publicado: (2025)
por: Alexander, Yotam, et al.
Publicado: (2025)
Corner Gradient Descent
por: Yarotsky, Dmitry
Publicado: (2025)
por: Yarotsky, Dmitry
Publicado: (2025)
Learning Tree-Based Models with Gradient Descent
por: Marton, Sascha
Publicado: (2026)
por: Marton, Sascha
Publicado: (2026)
Learning Associative Memories with Gradient Descent
por: Cabannes, Vivien, et al.
Publicado: (2024)
por: Cabannes, Vivien, et al.
Publicado: (2024)
In-context Learning and Gradient Descent Revisited
por: Deutch, Gilad, et al.
Publicado: (2023)
por: Deutch, Gilad, et al.
Publicado: (2023)
Stacking as Accelerated Gradient Descent
por: Agarwal, Naman, et al.
Publicado: (2024)
por: Agarwal, Naman, et al.
Publicado: (2024)
Growing Neural Networks: Dynamic Evolution through Gradient Descent
por: Radhakrishnan, Anil, et al.
Publicado: (2025)
por: Radhakrishnan, Anil, et al.
Publicado: (2025)
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
por: Cayci, Semih, et al.
Publicado: (2024)
por: Cayci, Semih, et al.
Publicado: (2024)
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
por: Padmanabha, Govinda Anantha, et al.
Publicado: (2024)
por: Padmanabha, Govinda Anantha, et al.
Publicado: (2024)
RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
por: Fang, Cheng, et al.
Publicado: (2025)
por: Fang, Cheng, et al.
Publicado: (2025)
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks
por: Li, Binghui, et al.
Publicado: (2024)
por: Li, Binghui, et al.
Publicado: (2024)
Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
por: Xu, Xianliang, et al.
Publicado: (2024)
por: Xu, Xianliang, et al.
Publicado: (2024)
Is All Learning (Natural) Gradient Descent?
por: Shoji, Lucas, et al.
Publicado: (2024)
por: Shoji, Lucas, et al.
Publicado: (2024)
Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training
por: Ma, Yuhan, et al.
Publicado: (2024)
por: Ma, Yuhan, et al.
Publicado: (2024)
Towards Learning Stochastic Population Models by Gradient Descent
por: Kreikemeyer, Justin N., et al.
Publicado: (2024)
por: Kreikemeyer, Justin N., et al.
Publicado: (2024)
Personalized Federated Learning with Exact Stochastic Gradient Descent
por: Nikoloutsopoulos, Sotirios, et al.
Publicado: (2022)
por: Nikoloutsopoulos, Sotirios, et al.
Publicado: (2022)
Gradient Descent with Provably Tuned Learning-rate Schedules
por: Sharma, Dravyansh
Publicado: (2025)
por: Sharma, Dravyansh
Publicado: (2025)
Learning Curves of Stochastic Gradient Descent in Kernel Regression
por: Zhang, Haihan, et al.
Publicado: (2025)
por: Zhang, Haihan, et al.
Publicado: (2025)
Partially Lazy Gradient Descent for Smoothed Online Learning
por: Mhaisen, Naram, et al.
Publicado: (2026)
por: Mhaisen, Naram, et al.
Publicado: (2026)
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
por: Cheng, Nuojin, et al.
Publicado: (2025)
por: Cheng, Nuojin, et al.
Publicado: (2025)
Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
por: Liu, Weihua, et al.
Publicado: (2024)
por: Liu, Weihua, et al.
Publicado: (2024)
Ejemplares similares
-
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence
por: Hsiao, Yen-Che, et al.
Publicado: (2024) -
On the Theory of Continual Learning with Gradient Descent for Neural Networks
por: Taheri, Hossein, et al.
Publicado: (2025) -
Efficient Search for Customized Activation Functions with Gradient Descent
por: Strack, Lukas, et al.
Publicado: (2024) -
Stochastic Gradient Descent with Adaptive Data
por: Che, Ethan, et al.
Publicado: (2024) -
Stochastic Gradient Descent for Two-layer Neural Networks
por: Cao, Dinghao, et al.
Publicado: (2024)