Mathematical analysis of one-layer neural network with fixed biases, a new activation function and other observations
Fuente:
arXiv
Saved in:
| Main Authors: | Macià, Fabricio, Nakamura, Shu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
by: Liu, Yu, et al.
Published: (2025)
by: Liu, Yu, et al.
Published: (2025)
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
by: Barboni, Raphaël, et al.
Published: (2025)
by: Barboni, Raphaël, et al.
Published: (2025)
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations
by: Carrasco, Pablo, et al.
Published: (2024)
by: Carrasco, Pablo, et al.
Published: (2024)
An analysis of optimization problems involving ReLU neural networks
by: Plate, Christoph, et al.
Published: (2025)
by: Plate, Christoph, 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)
The duality structure gradient descent algorithm: analysis and applications to neural networks
by: Flynn, Thomas
Published: (2017)
by: Flynn, Thomas
Published: (2017)
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)
Memory capacity of two layer neural networks with smooth activations
by: Madden, Liam, et al.
Published: (2023)
by: Madden, Liam, et al.
Published: (2023)
Convergence of gradient flow for learning convolutional neural networks
by: Diederen, Jona-Maria, et al.
Published: (2026)
by: Diederen, Jona-Maria, et al.
Published: (2026)
Size and depth of monotone neural networks: interpolation and approximation
by: Mikulincer, Dan, et al.
Published: (2022)
by: Mikulincer, Dan, et al.
Published: (2022)
Quadratic models for understanding catapult dynamics of neural networks
by: Zhu, Libin, et al.
Published: (2022)
by: Zhu, Libin, et al.
Published: (2022)
Reliably-stabilizing piecewise-affine neural network controllers
by: Fabiani, Filippo, et al.
Published: (2021)
by: Fabiani, Filippo, et al.
Published: (2021)
Formulations and scalability of neural network surrogates in nonlinear optimization problems
by: Parker, Robert B., et al.
Published: (2024)
by: Parker, Robert B., et al.
Published: (2024)
Learning to accelerate distributed ADMM using graph neural networks
by: Doerks, Henri, et al.
Published: (2025)
by: Doerks, Henri, et al.
Published: (2025)
A constrained optimization approach to improve robustness of neural networks
by: Zhao, Shudian, et al.
Published: (2024)
by: Zhao, Shudian, et al.
Published: (2024)
Approximation and interpolation of deep neural networks
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)
by: Constantinescu, Vlad-Raul, et al.
Published: (2023)
Path-conditioned training: a principled way to rescale ReLU neural networks
by: Lebeurrier, Arthur, et al.
Published: (2026)
by: Lebeurrier, Arthur, et al.
Published: (2026)
Solving a class of stochastic optimal control problems by physics-informed neural networks
by: Jiao, Zhe, et al.
Published: (2024)
by: Jiao, Zhe, et al.
Published: (2024)
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
by: Stops, Laura, et al.
Published: (2022)
by: Stops, Laura, et al.
Published: (2022)
Global optimization of graph acquisition functions for neural architecture search
by: Xie, Yilin, et al.
Published: (2025)
by: Xie, Yilin, et al.
Published: (2025)
Tuning the burn-in phase in training recurrent neural networks improves their performance
by: Schiller, Julian D., et al.
Published: (2026)
by: Schiller, Julian D., et al.
Published: (2026)
A neural network-based approach to hybrid systems identification for control
by: Fabiani, Filippo, et al.
Published: (2024)
by: Fabiani, Filippo, 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)
MIQCQP reformulation of the ReLU neural networks Lipschitz constant estimation problem
by: Sbihi, Mohammed, et al.
Published: (2024)
by: Sbihi, Mohammed, et al.
Published: (2024)
Verifying message-passing neural networks via topology-based bounds tightening
by: Hojny, Christopher, et al.
Published: (2024)
by: Hojny, Christopher, et al.
Published: (2024)
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)
Approximate non-linear model predictive control with safety-augmented neural networks
by: Hose, Henrik, et al.
Published: (2023)
by: Hose, Henrik, et al.
Published: (2023)
Efficient model predictive control for nonlinear systems modelled by deep neural networks
by: Lan, Jianglin
Published: (2024)
by: Lan, Jianglin
Published: (2024)
Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
by: Fabiani, Filippo, et al.
Published: (2022)
by: Fabiani, Filippo, et al.
Published: (2022)
On bounds for norms of reparameterized ReLU artificial neural network parameters: sums of fractional powers of the Lipschitz norm control the network parameter vector
by: Jentzen, Arnulf, et al.
Published: (2022)
by: Jentzen, Arnulf, et al.
Published: (2022)
Learning to Configure Mathematical Programming Solvers by Mathematical Programming
by: Iommazzo, Gabriele, et al.
Published: (2024)
by: Iommazzo, Gabriele, et al.
Published: (2024)
Graph neural networks for the prediction of molecular structure-property relationships
by: Rittig, Jan G., et al.
Published: (2022)
by: Rittig, Jan G., et al.
Published: (2022)
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)
Mitigating optimistic bias in entropic risk estimation and optimization
by: Sadana, Utsav, et al.
Published: (2024)
by: Sadana, Utsav, et al.
Published: (2024)
Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)
by: Jentzen, Arnulf, et al.
Published: (2024)
Mathematical Foundations of Deep Learning
by: Ye, Xiaojing
Published: (2026)
by: Ye, Xiaojing
Published: (2026)
ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining
by: Sharman, Bharat, et al.
Published: (2025)
by: Sharman, Bharat, et al.
Published: (2025)
Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
by: Wu, Long, et al.
Published: (2024)
by: Wu, Long, et al.
Published: (2024)
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
by: Lim, Dong-Young, et al.
Published: (2021)
by: Lim, Dong-Young, et al.
Published: (2021)
Employing Federated Learning for Training Autonomous HVAC Systems
by: Hagström, Fredrik, et al.
Published: (2024)
by: Hagström, Fredrik, et al.
Published: (2024)
Similar Items
-
ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
by: Liu, Yu, et al.
Published: (2025) -
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
by: Barboni, Raphaël, et al.
Published: (2025) -
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations
by: Carrasco, Pablo, et al.
Published: (2024) -
An analysis of optimization problems involving ReLU neural networks
by: Plate, Christoph, 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)