A multiobjective continuation method to compute the regularization path of deep neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | Amakor, Augustina C., Sonntag, Konstantin, Peitz, Sebastian |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Common pitfalls to avoid while using multiobjective optimization in machine learning
by: Akhter, Junaid, et al.
Published: (2024)
by: Akhter, Junaid, et al.
Published: (2024)
Surrogate-assisted multi-objective design of complex multibody systems
by: Amakor, Augustina C., et al.
Published: (2024)
by: Amakor, Augustina C., et al.
Published: (2024)
Multi-Objective Optimization for Sparse Deep Multi-Task Learning
by: Hotegni, S. S., et al.
Published: (2023)
by: Hotegni, S. S., et al.
Published: (2023)
Efficient and provably convergent end-to-end training of deep neural networks with linear constraints
by: Yang, Zonglin, et al.
Published: (2026)
by: Yang, Zonglin, et al.
Published: (2026)
Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
by: Grontas, Panagiotis D., et al.
Published: (2025)
by: Grontas, Panagiotis D., et al.
Published: (2025)
Recurrent neural networks: vanishing and exploding gradients are not the end of the story
by: Zucchet, Nicolas, et al.
Published: (2024)
by: Zucchet, Nicolas, et al.
Published: (2024)
Prodigy: An Expeditiously Adaptive Parameter-Free Learner
by: Mishchenko, Konstantin, et al.
Published: (2023)
by: Mishchenko, Konstantin, et al.
Published: (2023)
Convergence of continuous-time stochastic gradient descent with applications to deep neural networks
by: Lugosi, Gabor, et al.
Published: (2024)
by: Lugosi, Gabor, et al.
Published: (2024)
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
by: Peitz, Sebastian, et al.
Published: (2024)
by: Peitz, Sebastian, et al.
Published: (2024)
Explicit neural network classifiers for non-separable data
by: Ewald, Patrícia Muñoz
Published: (2025)
by: Ewald, Patrícia Muñoz
Published: (2025)
A second-order-like optimizer with adaptive gradient scaling for deep learning
by: Bolte, Jérôme, et al.
Published: (2024)
by: Bolte, Jérôme, et al.
Published: (2024)
Precise gradient descent training dynamics for finite-width multi-layer neural networks
by: Han, Qiyang, et al.
Published: (2025)
by: Han, Qiyang, et al.
Published: (2025)
Lagrangian Index Policy for Restless Bandits with Average Reward
by: Avrachenkov, Konstantin, et al.
Published: (2024)
by: Avrachenkov, Konstantin, et al.
Published: (2024)
The Road Less Scheduled
by: Defazio, Aaron, et al.
Published: (2024)
by: Defazio, Aaron, et al.
Published: (2024)
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
by: Ju, Caleb, et al.
Published: (2024)
by: Ju, Caleb, et al.
Published: (2024)
Frugality in second-order optimization: floating-point approximations for Newton's method
by: Carrino, Giuseppe, et al.
Published: (2025)
by: Carrino, Giuseppe, et al.
Published: (2025)
Approximation and interpolation of deep neural networks
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)
Towards Faster Decentralized Stochastic Optimization with Communication Compression
by: Islamov, Rustem, et al.
Published: (2024)
by: Islamov, Rustem, et al.
Published: (2024)
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
by: Ding, Dongsheng, et al.
Published: (2022)
by: Ding, Dongsheng, et al.
Published: (2022)
An Approximate Ascent Approach To Prove Convergence of PPO
by: Doering, Leif, et al.
Published: (2026)
by: Doering, Leif, et al.
Published: (2026)
Efficient model predictive control for nonlinear systems modelled by deep neural networks
by: Lan, Jianglin
Published: (2024)
by: Lan, Jianglin
Published: (2024)
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
by: Wagner, Dominik, et al.
Published: (2024)
by: Wagner, Dominik, et al.
Published: (2024)
Interpretable global minima of deep ReLU neural networks on sequentially separable data
by: Chen, Thomas, et al.
Published: (2024)
by: Chen, Thomas, et al.
Published: (2024)
Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramér Surrogate
by: C., Simo Alami, et al.
Published: (2025)
by: C., Simo Alami, et al.
Published: (2025)
Improved Physics-informed neural networks loss function regularization with a variance-based term
by: Hanna, John M., et al.
Published: (2024)
by: Hanna, John M., et al.
Published: (2024)
Deep Reinforcement Learning for Traveling Purchaser Problems
by: Yuan, Haofeng, et al.
Published: (2024)
by: Yuan, Haofeng, et al.
Published: (2024)
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
by: An, Jing, et al.
Published: (2023)
by: An, Jing, et al.
Published: (2023)
A multilevel approach to accelerate the training of Transformers
by: Lauga, Guillaume, et al.
Published: (2025)
by: Lauga, Guillaume, et al.
Published: (2025)
A Minimalist Bayesian Framework for Stochastic Optimization
by: Wang, Kaizheng
Published: (2025)
by: Wang, Kaizheng
Published: (2025)
Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping
by: Sonntag, Konstantin, et al.
Published: (2023)
by: Sonntag, Konstantin, et al.
Published: (2023)
A Rod Flow Model for Adam at the Edge of Stability
by: Regis, Eric, et al.
Published: (2026)
by: Regis, Eric, et al.
Published: (2026)
A Novel Unified Parametric Assumption for Nonconvex Optimization
by: Riabinin, Artem, et al.
Published: (2025)
by: Riabinin, Artem, et al.
Published: (2025)
YuriiFormer: A Suite of Nesterov-Accelerated Transformers
by: Zimin, Aleksandr, et al.
Published: (2026)
by: Zimin, Aleksandr, et al.
Published: (2026)
Effective Frontiers: A Unification of Neural Scaling Laws
by: Zou, Jiaxuan, et al.
Published: (2026)
by: Zou, Jiaxuan, et al.
Published: (2026)
A Unified Framework for Gradient Aggregation in Multi-Objective Optimization
by: Hu, Zeou, et al.
Published: (2026)
by: Hu, Zeou, et al.
Published: (2026)
ARO: A New Lens On Matrix Optimization For Large Models
by: Gong, Wenbo, et al.
Published: (2026)
by: Gong, Wenbo, et al.
Published: (2026)
A Median Perspective on Unlabeled Data for Out-of-Distribution Detection
by: Abbas, Momin, et al.
Published: (2025)
by: Abbas, Momin, et al.
Published: (2025)
Stochastic Optimization with Constraints: A Non-asymptotic Instance-Dependent Analysis
by: Khamaru, Koulik
Published: (2024)
by: Khamaru, Koulik
Published: (2024)
A Convexity-dependent Two-Phase Training Algorithm for Deep Neural Networks
by: Hrycej, Tomas, et al.
Published: (2025)
by: Hrycej, Tomas, et al.
Published: (2025)
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
by: Sharifnassab, Arsalan, et al.
Published: (2024)
by: Sharifnassab, Arsalan, et al.
Published: (2024)
Similar Items
-
Common pitfalls to avoid while using multiobjective optimization in machine learning
by: Akhter, Junaid, et al.
Published: (2024) -
Surrogate-assisted multi-objective design of complex multibody systems
by: Amakor, Augustina C., et al.
Published: (2024) -
Multi-Objective Optimization for Sparse Deep Multi-Task Learning
by: Hotegni, S. S., et al.
Published: (2023) -
Efficient and provably convergent end-to-end training of deep neural networks with linear constraints
by: Yang, Zonglin, et al.
Published: (2026) -
Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
by: Grontas, Panagiotis D., et al.
Published: (2025)