Copertina

BARNN: A Bayesian Autoregressive and Recurrent Neural Network

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Coscia, Dario, Welling, Max, Demo, Nicola, Rozza, Gianluigi
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Dimensionality Reduction Approach for Convolutional Neural Networks
di: Meneghetti, Laura, et al.
Pubblicazione: (2021)

Generative Adversarial Reduced Order Modelling
di: Coscia, Dario, et al.
Pubblicazione: (2023)

BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
di: Coscia, Dario, et al.
Pubblicazione: (2026)

Flowing with Confidence
di: de Kruiff, Friso, et al.
Pubblicazione: (2026)

BLIPs: Bayesian Learned Interatomic Potentials
di: Coscia, Dario, et al.
Pubblicazione: (2025)

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
di: Ivagnes, Anna, et al.
Pubblicazione: (2022)

An extended physics informed neural network for preliminary analysis of parametric optimal control problems
di: Demo, Nicola, et al.
Pubblicazione: (2021)

A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
di: Meneghetti, Laura, et al.
Pubblicazione: (2022)

Artificial Kuramoto Oscillatory Neurons
di: Miyato, Takeru, et al.
Pubblicazione: (2024)

Generative System Dynamics in Recurrent Neural Networks
di: Casoni, Michele, et al.
Pubblicazione: (2025)

Precise Bayesian Neural Networks
di: Brito, Carlos Stein
Pubblicazione: (2025)

Neural Network Plasticity and Loss Sharpness
di: Koster, Max, et al.
Pubblicazione: (2024)

Latent Bayesian Optimization via Autoregressive Normalizing Flows
di: Lee, Seunghun, et al.
Pubblicazione: (2025)

Fast Training of Recurrent Neural Networks with Stationary State Feedbacks
di: Caillon, Paul, et al.
Pubblicazione: (2025)

Modular Boundaries in Recurrent Neural Networks
di: Tanner, Jacob, et al.
Pubblicazione: (2023)

Spectral Theory for Edge Pruning in Asynchronous Recurrent Graph Neural Networks
di: Bessone, Nicolas
Pubblicazione: (2025)

Causal Graph Neural Networks for Healthcare
di: Mesinovic, Munib, et al.
Pubblicazione: (2025)

Stochastic Weight Sharing for Bayesian Neural Networks
di: Lin, Moule, et al.
Pubblicazione: (2025)

Full Bayesian Significance Testing for Neural Networks
di: Liu, Zehua, et al.
Pubblicazione: (2024)

Incorporating Unlabelled Data into Bayesian Neural Networks
di: Sharma, Mrinank, et al.
Pubblicazione: (2023)

Regularizing Explanations in Bayesian Convolutional Neural Networks
di: Bekkemoen, Yanzhe, et al.
Pubblicazione: (2021)

Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networks
di: Yin, Bojian, et al.
Pubblicazione: (2024)

Differentiated Thyroid Cancer Recurrence Classification Using Machine Learning Models and Bayesian Neural Networks with Varying Priors: A SHAP-Based Interpretation of the Best Performing Model
di: Kumari, HMNS, et al.
Pubblicazione: (2025)

Bayesian Neural Networks: A Min-Max Game Framework
di: Hong, Junping, et al.
Pubblicazione: (2023)

Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization
di: Chaubard, Francois, et al.
Pubblicazione: (2025)

Tubular Riemannian Laplace Approximations for Bayesian Neural Networks
di: David, Rodrigo Pereira
Pubblicazione: (2025)

Deep Manifold Part 2: Neural Network Mathematics
di: Ma, Max Y., et al.
Pubblicazione: (2025)

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
di: Xiao, Tim Z., et al.
Pubblicazione: (2023)

Energy-based Autoregressive Generation for Neural Population Dynamics
di: Ge, Ningling, et al.
Pubblicazione: (2025)

Recurrent Aggregators in Neural Algorithmic Reasoning
di: Xu, Kaijia, et al.
Pubblicazione: (2024)

Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks
di: Wang, Runzhong, et al.
Pubblicazione: (2024)

Identifying Information-Transfer Nodes in a Recurrent Neural Network Reveals Dynamic Representations
di: Hintze, Arend, et al.
Pubblicazione: (2025)

IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction
di: Zhou, Yifan, et al.
Pubblicazione: (2025)

Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation
di: Qian, Linglong, et al.
Pubblicazione: (2024)

Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
di: Smyl, Slawek, et al.
Pubblicazione: (2026)

Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
di: Li, Jinhao, et al.
Pubblicazione: (2026)

Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery
di: Zheng, Yuhan, et al.
Pubblicazione: (2024)

Deep Manifold Part 1: Anatomy of Neural Network Manifold
di: Ma, Max Y., et al.
Pubblicazione: (2024)

Protect Your Score: Contact Tracing With Differential Privacy Guarantees
di: Romijnders, Rob, et al.
Pubblicazione: (2023)

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
di: Wang, Shida, et al.
Pubblicazione: (2023)