Guardado en:
| Autores principales: | de Araújo, Gracielle Antunes, Gonçalves, Flávio B. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2602.22492 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Convergence of Shallow ReLU Networks on Weakly Interacting Data
por: Dana, Léo, et al.
Publicado: (2025)
por: Dana, Léo, et al.
Publicado: (2025)
Towards Initialization-dependent and Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks
por: Lei, Yunwen, et al.
Publicado: (2026)
por: Lei, Yunwen, et al.
Publicado: (2026)
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
por: Kiroriwal, Saksham, et al.
Publicado: (2025)
por: Kiroriwal, Saksham, et al.
Publicado: (2025)
Bayesian Analysis of Combinatorial Gaussian Process Bandits
por: Sandberg, Jack, et al.
Publicado: (2023)
por: Sandberg, Jack, et al.
Publicado: (2023)
Gradient Flow Convergence Guarantee for General Neural Network Architectures
por: Jakhmola, Yash
Publicado: (2025)
por: Jakhmola, Yash
Publicado: (2025)
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
por: Schmitt, Marvin, et al.
Publicado: (2024)
por: Schmitt, Marvin, et al.
Publicado: (2024)
Quotient Geometry, Effective Curvature, and Implicit Bias in Simple Shallow Neural Networks
por: Dong, Hang-Cheng, et al.
Publicado: (2026)
por: Dong, Hang-Cheng, et al.
Publicado: (2026)
The Spectral Bias of Shallow Neural Network Learning is Shaped by the Choice of Non-linearity
por: Sahs, Justin, et al.
Publicado: (2025)
por: Sahs, Justin, et al.
Publicado: (2025)
A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
por: James, Zachary, et al.
Publicado: (2025)
por: James, Zachary, et al.
Publicado: (2025)
Scalable Neural Network Kernels
por: Sehanobish, Arijit, et al.
Publicado: (2023)
por: Sehanobish, Arijit, et al.
Publicado: (2023)
Gaussian Process Neural Additive Models
por: Zhang, Wei, et al.
Publicado: (2024)
por: Zhang, Wei, et al.
Publicado: (2024)
From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network
por: Amirkhanian, Hayk, et al.
Publicado: (2025)
por: Amirkhanian, Hayk, et al.
Publicado: (2025)
UnIT: Scalable Unstructured Inference-Time Pruning for MAC-efficient Neural Inference on MCUs
por: Neth, Ashe, et al.
Publicado: (2025)
por: Neth, Ashe, et al.
Publicado: (2025)
Learning to Condition: A Neural Heuristic for Scalable MPE Inference
por: Malhotra, Brij, et al.
Publicado: (2025)
por: Malhotra, Brij, et al.
Publicado: (2025)
Precise Bayesian Neural Networks
por: Brito, Carlos Stein
Publicado: (2025)
por: Brito, Carlos Stein
Publicado: (2025)
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
por: Saad, Feras, et al.
Publicado: (2024)
por: Saad, Feras, et al.
Publicado: (2024)
Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles
por: Liu, Shengcai, et al.
Publicado: (2025)
por: Liu, Shengcai, et al.
Publicado: (2025)
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
por: Xu, Jian, et al.
Publicado: (2024)
por: Xu, Jian, et al.
Publicado: (2024)
Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process
por: Saito, Issei, et al.
Publicado: (2025)
por: Saito, Issei, et al.
Publicado: (2025)
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
por: Antoran, Javier
Publicado: (2024)
por: Antoran, Javier
Publicado: (2024)
Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization
por: Aguilar, Leonel, et al.
Publicado: (2026)
por: Aguilar, Leonel, et al.
Publicado: (2026)
On the Utilization of Unique Node Identifiers in Graph Neural Networks
por: Bechler-Speicher, Maya, et al.
Publicado: (2024)
por: Bechler-Speicher, Maya, et al.
Publicado: (2024)
Scalable Evaluation and Neural Models for Compositional Generalization
por: Camposampiero, Giacomo, et al.
Publicado: (2025)
por: Camposampiero, Giacomo, et al.
Publicado: (2025)
Soft Contamination Means Benchmarks Test Shallow Generalization
por: Spiesberger, Ari, et al.
Publicado: (2026)
por: Spiesberger, Ari, et al.
Publicado: (2026)
On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks
por: Yan, Mingsong, et al.
Publicado: (2025)
por: Yan, Mingsong, et al.
Publicado: (2025)
Stochastic Weight Sharing for Bayesian Neural Networks
por: Lin, Moule, et al.
Publicado: (2025)
por: Lin, Moule, et al.
Publicado: (2025)
Full Bayesian Significance Testing for Neural Networks
por: Liu, Zehua, et al.
Publicado: (2024)
por: Liu, Zehua, et al.
Publicado: (2024)
Incorporating Unlabelled Data into Bayesian Neural Networks
por: Sharma, Mrinank, et al.
Publicado: (2023)
por: Sharma, Mrinank, et al.
Publicado: (2023)
Regularizing Explanations in Bayesian Convolutional Neural Networks
por: Bekkemoen, Yanzhe, et al.
Publicado: (2021)
por: Bekkemoen, Yanzhe, et al.
Publicado: (2021)
Entropic Causal Inference: Graph Identifiability
por: Compton, Spencer, et al.
Publicado: (2025)
por: Compton, Spencer, et al.
Publicado: (2025)
From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces
por: Xie, Zikai, et al.
Publicado: (2025)
por: Xie, Zikai, et al.
Publicado: (2025)
Derivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process
por: Watanabe, Shuhei
Publicado: (2025)
por: Watanabe, Shuhei
Publicado: (2025)
Free Energy Manifold: Score-Based Inference for Hybrid Bayesian Networks
por: Park, Cheol Young, et al.
Publicado: (2026)
por: Park, Cheol Young, et al.
Publicado: (2026)
Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work
por: Irwin, Christopher, et al.
Publicado: (2024)
por: Irwin, Christopher, et al.
Publicado: (2024)
Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking
por: Kolomvaki, Afroditi, et al.
Publicado: (2026)
por: Kolomvaki, Afroditi, et al.
Publicado: (2026)
Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
por: Defilippis, Leonardo, et al.
Publicado: (2025)
por: Defilippis, Leonardo, et al.
Publicado: (2025)
ReMAP: Neural Reparameterization for Scalable MAP Inference in Arbitrary-Order Markov Random Fields
por: Wang, Yaomin, et al.
Publicado: (2024)
por: Wang, Yaomin, et al.
Publicado: (2024)
BARNN: A Bayesian Autoregressive and Recurrent Neural Network
por: Coscia, Dario, et al.
Publicado: (2025)
por: Coscia, Dario, et al.
Publicado: (2025)
Tubular Riemannian Laplace Approximations for Bayesian Neural Networks
por: David, Rodrigo Pereira
Publicado: (2025)
por: David, Rodrigo Pereira
Publicado: (2025)
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
por: Younesian, Taraneh, et al.
Publicado: (2023)
por: Younesian, Taraneh, et al.
Publicado: (2023)
Ejemplares similares
-
Convergence of Shallow ReLU Networks on Weakly Interacting Data
por: Dana, Léo, et al.
Publicado: (2025) -
Towards Initialization-dependent and Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks
por: Lei, Yunwen, et al.
Publicado: (2026) -
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
por: Kiroriwal, Saksham, et al.
Publicado: (2025) -
Bayesian Analysis of Combinatorial Gaussian Process Bandits
por: Sandberg, Jack, et al.
Publicado: (2023) -
Gradient Flow Convergence Guarantee for General Neural Network Architectures
por: Jakhmola, Yash
Publicado: (2025)