A lift for input-convex neural network training
Fuente:
arXiv
Saved in:
| Main Authors: | Siahkoohi, Ali, Thatipelli, Anirudh |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Hypernetwork-based approach for grid-independent functional data clustering
by: Thatipelli, Anirudh, et al.
Published: (2026)
by: Thatipelli, Anirudh, et al.
Published: (2026)
XConv: Low-memory stochastic backpropagation for convolutional layers
by: Thatipelli, Anirudh, et al.
Published: (2021)
by: Thatipelli, Anirudh, et al.
Published: (2021)
Conditional neural control variates for variance reduction in Bayesian inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)
by: Siahkoohi, Ali, et al.
Published: (2026)
On the role of memorization in learned priors for geophysical inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)
by: Siahkoohi, Ali, et al.
Published: (2026)
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)
Dual-space posterior sampling for Bayesian inference in constrained inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)
by: Siahkoohi, Ali, et al.
Published: (2026)
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)
Multi-task neural networks by learned contextual inputs
by: Sandnes, Anders T., et al.
Published: (2023)
by: Sandnes, Anders T., et al.
Published: (2023)
Asymptotic convexity of wide and shallow neural networks
by: Borkar, Vivek, et al.
Published: (2025)
by: Borkar, Vivek, et al.
Published: (2025)
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
by: Orozco, Rafael, et al.
Published: (2024)
by: Orozco, Rafael, et al.
Published: (2024)
Aspects of holographic entanglement using physics-informed-neural-networks
by: Deb, Anirudh, et al.
Published: (2025)
by: Deb, Anirudh, et al.
Published: (2025)
Intrinsic training dynamics of deep neural networks
by: Marcotte, Sibylle, et al.
Published: (2025)
by: Marcotte, Sibylle, et al.
Published: (2025)
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)
DeepCFD: Efficient near-ground airfoil lift coefficient approximation with deep convolutional neural networks
by: Esabat, Mohammad Amin, et al.
Published: (2025)
by: Esabat, Mohammad Amin, et al.
Published: (2025)
On shallow feedforward neural networks with inputs from a topological space
by: Ismailov, Vugar
Published: (2025)
by: Ismailov, Vugar
Published: (2025)
MIRA: Towards Mitigating Reward Hacking in Inference-Time Alignment of T2I Diffusion Models
by: Zhai, Kevin, et al.
Published: (2025)
by: Zhai, Kevin, et al.
Published: (2025)
On Feynman--Kac training of partial Bayesian neural networks
by: Zhao, Zheng, et al.
Published: (2023)
by: Zhao, Zheng, et al.
Published: (2023)
Guiding the retraining of convolutional neural networks against adversarial inputs
by: López, Francisco Durán, et al.
Published: (2022)
by: López, Francisco Durán, et al.
Published: (2022)
Central limit theorems for the outputs of fully convolutional neural networks with time series input
by: Betken, Annika, et al.
Published: (2026)
by: Betken, Annika, et al.
Published: (2026)
A result relating convex n-widths to covering numbers with some applications to neural networks
by: Baxter, Jonathan, et al.
Published: (2025)
by: Baxter, Jonathan, et al.
Published: (2025)
Gradient-free training of recurrent neural networks
by: Bolager, Erik Lien, et al.
Published: (2024)
by: Bolager, Erik Lien, et al.
Published: (2024)
Rapid training of quantum recurrent neural networks
by: Siemaszko, Michał, et al.
Published: (2022)
by: Siemaszko, Michał, et al.
Published: (2022)
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)
Strategies for training point distributions in physics-informed neural networks
by: Humagain, Santosh, et al.
Published: (2025)
by: Humagain, Santosh, et al.
Published: (2025)
Comparison of neural network training strategies for the simulation of dynamical systems
by: Strasser, Paul, et al.
Published: (2025)
by: Strasser, Paul, et al.
Published: (2025)
Photonic convolutional neural network with pre-trained in-situ training
by: Ranjan, Saurabh, et al.
Published: (2026)
by: Ranjan, Saurabh, et al.
Published: (2026)
Generating adversarial inputs for a graph neural network model of AC power flow
by: Parker, Robert
Published: (2026)
by: Parker, Robert
Published: (2026)
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
by: Bigi, Filippo, et al.
Published: (2024)
by: Bigi, Filippo, et al.
Published: (2024)
An alternative approach to train neural networks using monotone variational inequality
by: Xu, Chen, et al.
Published: (2022)
by: Xu, Chen, et al.
Published: (2022)
Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
by: Shi, Zhongjie, et al.
Published: (2024)
by: Shi, Zhongjie, et al.
Published: (2024)
Robustness in sparse artificial neural networks trained with adaptive topology
by: Sulyok, Bendegúz, et al.
Published: (2026)
by: Sulyok, Bendegúz, et al.
Published: (2026)
Confidence-gated training for efficient early-exit neural networks
by: Mokssit, Saad, et al.
Published: (2025)
by: Mokssit, Saad, et al.
Published: (2025)
Adaptive multiple optimal learning factors for neural network training
by: Challagundla, Jeshwanth
Published: (2024)
by: Challagundla, Jeshwanth
Published: (2024)
Simmering: Sufficient is better than optimal for training neural networks
by: Babayan, Irina, et al.
Published: (2024)
by: Babayan, Irina, et al.
Published: (2024)
Quantitative convergence of trained single layer neural networks to Gaussian processes
by: Mosig, Eloy, et al.
Published: (2025)
by: Mosig, Eloy, et al.
Published: (2025)
A discrete physics-informed training for projection-based reduced order models with neural networks
by: Sibuet, N., et al.
Published: (2025)
by: Sibuet, N., 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)
A rationale from frequency perspective for grokking in training neural network
by: Zhou, Zhangchen, et al.
Published: (2024)
by: Zhou, Zhangchen, et al.
Published: (2024)
An experimental comparative study of backpropagation and alternatives for training binary neural networks for image classification
by: Crulis, Ben, et al.
Published: (2024)
by: Crulis, Ben, et al.
Published: (2024)
Asymmetrical estimator for training encapsulated deep photonic neural networks
by: Wang, Yizhi, et al.
Published: (2024)
by: Wang, Yizhi, et al.
Published: (2024)
Similar Items
-
Hypernetwork-based approach for grid-independent functional data clustering
by: Thatipelli, Anirudh, et al.
Published: (2026) -
XConv: Low-memory stochastic backpropagation for convolutional layers
by: Thatipelli, Anirudh, et al.
Published: (2021) -
Conditional neural control variates for variance reduction in Bayesian inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026) -
On the role of memorization in learned priors for geophysical inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026) -
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations
by: Carrasco, Pablo, et al.
Published: (2024)