Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
Fuente:
arXiv
Saved in:
| Main Authors: | Milsom, Edward, Anson, Ben, Aitchison, Laurence |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Convolutional Deep Kernel Machines
by: Milsom, Edward, et al.
Published: (2023)
by: Milsom, Edward, et al.
Published: (2023)
Flexible Infinite-Width Graph Convolutional Neural Networks
by: Anson, Ben, et al.
Published: (2024)
by: Anson, Ben, et al.
Published: (2024)
Function-Space Learning Rates
by: Milsom, Edward, et al.
Published: (2025)
by: Milsom, Edward, et al.
Published: (2025)
Controlling changes to attention logits
by: Anson, Ben, et al.
Published: (2025)
by: Anson, Ben, et al.
Published: (2025)
Scale-invariant Attention
by: Anson, Ben, et al.
Published: (2025)
by: Anson, Ben, et al.
Published: (2025)
Why you don't overfit, and don't need Bayes if you only train for one epoch
by: Aitchison, Laurence
Published: (2024)
by: Aitchison, Laurence
Published: (2024)
The Generalised Kernel Covariance Measure
by: Bergen, Luca, et al.
Published: (2026)
by: Bergen, Luca, et al.
Published: (2026)
Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods
by: Follain, Bertille, et al.
Published: (2024)
by: Follain, Bertille, et al.
Published: (2024)
Batch size invariant Adam
by: Wang, Xi, et al.
Published: (2024)
by: Wang, Xi, et al.
Published: (2024)
How to set AdamW's weight decay as you scale model and dataset size
by: Wang, Xi, et al.
Published: (2024)
by: Wang, Xi, et al.
Published: (2024)
Learning to Skip the Middle Layers of Transformers
by: Lawson, Tim, et al.
Published: (2025)
by: Lawson, Tim, et al.
Published: (2025)
Descriptive Kernel Convolution Network with Improved Random Walk Kernel
by: Lee, Meng-Chieh, et al.
Published: (2024)
by: Lee, Meng-Chieh, et al.
Published: (2024)
Improved Kernel Alignment Regret Bound for Online Kernel Learning
by: Li, Junfan, et al.
Published: (2022)
by: Li, Junfan, et al.
Published: (2022)
The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines
by: Zhang, Di, et al.
Published: (2024)
by: Zhang, Di, et al.
Published: (2024)
Generalisation of RLHF under Reward Shift and Clipped KL Regularisation
by: Tang, Kenton, et al.
Published: (2026)
by: Tang, Kenton, et al.
Published: (2026)
Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis
by: Farghly, Tyler, et al.
Published: (2025)
by: Farghly, Tyler, et al.
Published: (2025)
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
by: Incudini, Massimiliano, et al.
Published: (2022)
by: Incudini, Massimiliano, et al.
Published: (2022)
Deep Kernel Fusion for Transformers
by: Zhang, Zixi, et al.
Published: (2026)
by: Zhang, Zixi, et al.
Published: (2026)
Spectral Truncation Kernels: Noncommutativity in $C^*$-algebraic Kernel Machines
by: Hashimoto, Yuka, et al.
Published: (2024)
by: Hashimoto, Yuka, et al.
Published: (2024)
Using Neural Networks for Data Cleaning in Weather Datasets
by: Hanslope, Jack R. P., et al.
Published: (2024)
by: Hanslope, Jack R. P., et al.
Published: (2024)
Massively Parallel Expectation Maximization For Approximate Posteriors
by: Heap, Thomas, et al.
Published: (2025)
by: Heap, Thomas, et al.
Published: (2025)
Multiple Locally Linear Kernel Machines
by: Picard, David
Published: (2024)
by: Picard, David
Published: (2024)
Notes on Kernel Methods in Machine Learning
by: Pérez-Rosero, Diego Armando, et al.
Published: (2025)
by: Pérez-Rosero, Diego Armando, et al.
Published: (2025)
On the Nystrom Approximation for Preconditioning in Kernel Machines
by: Abedsoltan, Amirhesam, et al.
Published: (2023)
by: Abedsoltan, Amirhesam, et al.
Published: (2023)
One Class Restricted Kernel Machines
by: Quadir, A., et al.
Published: (2025)
by: Quadir, A., et al.
Published: (2025)
Improving the Weighting Strategy in KernelSHAP
by: Olsen, Lars Henry Berge, et al.
Published: (2024)
by: Olsen, Lars Henry Berge, et al.
Published: (2024)
Deep Hierarchical Graph Alignment Kernels
by: Tang, Shuhao, et al.
Published: (2024)
by: Tang, Shuhao, et al.
Published: (2024)
Analysis of Structured Deep Kernel Networks
by: Wenzel, Tizian, et al.
Published: (2021)
by: Wenzel, Tizian, et al.
Published: (2021)
Learning Curves of Stochastic Gradient Descent in Kernel Regression
by: Zhang, Haihan, et al.
Published: (2025)
by: Zhang, Haihan, et al.
Published: (2025)
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel
by: Zhu, Ye, et al.
Published: (2019)
by: Zhu, Ye, et al.
Published: (2019)
Correspondence of NNGP Kernel and the Matern Kernel
by: Muyskens, Amanda, et al.
Published: (2024)
by: Muyskens, Amanda, et al.
Published: (2024)
Kernel Stochastic Configuration Networks for Nonlinear Regression
by: Chen, Yongxuan, et al.
Published: (2024)
by: Chen, Yongxuan, et al.
Published: (2024)
The Stochastic Occupation Kernel Method for System Identification
by: Wells, Michael, et al.
Published: (2024)
by: Wells, Michael, et al.
Published: (2024)
Twin Restricted Kernel Machines for Multiview Classification
by: Quadir, A., et al.
Published: (2025)
by: Quadir, A., et al.
Published: (2025)
Recurrent Deep Kernel Learning of Dynamical Systems
by: Botteghi, Nicolò, et al.
Published: (2024)
by: Botteghi, Nicolò, et al.
Published: (2024)
Training-Free Generative Modeling via Kernelized Stochastic Interpolants
by: Coeurdoux, Florentin, et al.
Published: (2026)
by: Coeurdoux, Florentin, et al.
Published: (2026)
Deep Kernel Learning for Stratifying Glaucoma Trajectories
by: Rushing, Bruce, et al.
Published: (2026)
by: Rushing, Bruce, et al.
Published: (2026)
Response to Promises and Pitfalls of Deep Kernel Learning
by: Wilson, Andrew Gordon, et al.
Published: (2025)
by: Wilson, Andrew Gordon, et al.
Published: (2025)
Scalable Deep Basis Kernel Gaussian Processes
by: Zhu, Yunqin, et al.
Published: (2025)
by: Zhu, Yunqin, et al.
Published: (2025)
Improved Random Features for Dot Product Kernels
by: Wacker, Jonas, et al.
Published: (2022)
by: Wacker, Jonas, et al.
Published: (2022)
Similar Items
-
Convolutional Deep Kernel Machines
by: Milsom, Edward, et al.
Published: (2023) -
Flexible Infinite-Width Graph Convolutional Neural Networks
by: Anson, Ben, et al.
Published: (2024) -
Function-Space Learning Rates
by: Milsom, Edward, et al.
Published: (2025) -
Controlling changes to attention logits
by: Anson, Ben, et al.
Published: (2025) -
Scale-invariant Attention
by: Anson, Ben, et al.
Published: (2025)