Saved in:
| Main Authors: | Baxter, Jonathan, Bartlett, Peter |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.04912 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Reinforcement Learning in POMDP's via Direct Gradient Ascent
by: Baxter, Jonathan, et al.
Published: (2025)
by: Baxter, Jonathan, et al.
Published: (2025)
A lift for input-convex neural network training
by: Siahkoohi, Ali, et al.
Published: (2026)
by: Siahkoohi, Ali, et al.
Published: (2026)
A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024)
by: Zhao, Yingxue, et al.
Published: (2024)
Asymptotic convexity of wide and shallow neural networks
by: Borkar, Vivek, et al.
Published: (2025)
by: Borkar, Vivek, et al.
Published: (2025)
The Evolution of Learning Algorithms for Artificial Neural Networks
by: Baxter, Jonathan
Published: (2025)
by: Baxter, Jonathan
Published: (2025)
A TVD neural network closure and application to turbulent combustion
by: Suh, Seung Won, et al.
Published: (2024)
by: Suh, Seung Won, et al.
Published: (2024)
Exploring the loss landscape of regularized neural networks via convex duality
by: Kim, Sungyoon, et al.
Published: (2024)
by: Kim, Sungyoon, et al.
Published: (2024)
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)
Scaling Internal-State Policy-Gradient Methods for POMDPs
by: Aberdeen, Douglas, et al.
Published: (2025)
by: Aberdeen, Douglas, et al.
Published: (2025)
Reinforcement Learning From State and Temporal Differences
by: Weaver, Lex, et al.
Published: (2025)
by: Weaver, Lex, et al.
Published: (2025)
Differentiable neural network representation of multi-well, locally-convex potentials
by: Jones, Reese E., et al.
Published: (2025)
by: Jones, Reese E., et al.
Published: (2025)
Thermal transmittance prediction based on the application of artificial neural networks on heat flux method results
by: Gumbarević, Sanjin, et al.
Published: (2021)
by: Gumbarević, Sanjin, et al.
Published: (2021)
Symplectic convolutional neural networks
by: Yıldız, Süleyman, et al.
Published: (2025)
by: Yıldız, Süleyman, et al.
Published: (2025)
Towards graph neural networks for provably solving convex optimization problems
by: Qian, Chendi, et al.
Published: (2025)
by: Qian, Chendi, et al.
Published: (2025)
Prototype-enhanced prediction in graph neural networks for climate applications
by: Keshtmand, Nawid, et al.
Published: (2025)
by: Keshtmand, Nawid, et al.
Published: (2025)
Can a Transformer Represent a Kalman Filter?
by: Goel, Gautam, et al.
Published: (2023)
by: Goel, Gautam, et al.
Published: (2023)
Symmetry-enforcing neural networks with applications to constitutive modeling
by: Garanger, Kévin, et al.
Published: (2023)
by: Garanger, Kévin, et al.
Published: (2023)
Deep multitask neural networks for solving some stochastic optimal control problems
by: Yeo, Christian
Published: (2024)
by: Yeo, Christian
Published: (2024)
On convex decision regions in deep network representations
by: Tětková, Lenka, et al.
Published: (2023)
by: Tětková, Lenka, et al.
Published: (2023)
On quantitative Laplace-type convergence results for some exponential probability measures, with two applications
by: De Bortoli, Valentin, et al.
Published: (2021)
by: De Bortoli, Valentin, et al.
Published: (2021)
A Multi-Agent, Policy-Gradient approach to Network Routing
by: Tao, Nigel, et al.
Published: (2025)
by: Tao, Nigel, et al.
Published: (2025)
Engineering application of physics-informed neural networks for Saint-Venant torsion
by: Jo, Su Yeong, et al.
Published: (2025)
by: Jo, Su Yeong, et al.
Published: (2025)
Training morphological neural networks with gradient descent: some theoretical insights
by: Blusseau, Samy
Published: (2024)
by: Blusseau, Samy
Published: (2024)
On the effects of biased quantum random numbers on the initialization of artificial neural networks
by: Heese, Raoul, et al.
Published: (2021)
by: Heese, Raoul, et al.
Published: (2021)
Estimating the stability number of a random graph using convolutional neural networks
by: Davila, Randy
Published: (2024)
by: Davila, Randy
Published: (2024)
A graph neural network based chemical mechanism reduction method for combustion applications
by: Padiyar, Manuru Nithin, et al.
Published: (2026)
by: Padiyar, Manuru Nithin, et al.
Published: (2026)
The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks
by: Kawase, Yoshiaki
Published: (2025)
by: Kawase, Yoshiaki
Published: (2025)
Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)
by: Thakolkaran, Prakash, et al.
Published: (2025)
by: Thakolkaran, Prakash, et al.
Published: (2025)
A physics-informed neural network framework for modeling obstacle-related equations
by: Bahja, Hamid El, et al.
Published: (2023)
by: Bahja, Hamid El, et al.
Published: (2023)
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
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)
The duality structure gradient descent algorithm: analysis and applications to neural networks
by: Flynn, Thomas
Published: (2017)
by: Flynn, Thomas
Published: (2017)
Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)
by: Wu, Jingfeng, et al.
Published: (2025)
Training Dynamics of Softmax Self-Attention: Fast Global Convergence via Preconditioning
by: Goel, Gautam, et al.
Published: (2026)
by: Goel, Gautam, et al.
Published: (2026)
Contextual Bandits with Stage-wise Constraints
by: Pacchiano, Aldo, et al.
Published: (2024)
by: Pacchiano, Aldo, et al.
Published: (2024)
LEAPS: A discrete neural sampler via locally equivariant networks
by: Holderrieth, Peter, et al.
Published: (2025)
by: Holderrieth, Peter, et al.
Published: (2025)
Flexible inference for animal learning rules using neural networks
by: Liu, Yuhan Helena, et al.
Published: (2025)
by: Liu, Yuhan Helena, et al.
Published: (2025)
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
Efficient n-body simulations using physics informed graph neural networks
by: Ramos-Osuna, Víctor, et al.
Published: (2025)
by: Ramos-Osuna, Víctor, et al.
Published: (2025)
Similar Items
-
Reinforcement Learning in POMDP's via Direct Gradient Ascent
by: Baxter, Jonathan, et al.
Published: (2025) -
A lift for input-convex neural network training
by: Siahkoohi, Ali, et al.
Published: (2026) -
A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024) -
Asymptotic convexity of wide and shallow neural networks
by: Borkar, Vivek, et al.
Published: (2025) -
The Evolution of Learning Algorithms for Artificial Neural Networks
by: Baxter, Jonathan
Published: (2025)