Cover Image

Epistemic Uncertainty Quantification for Pre-trained VLMs via Riemannian Flow Matching

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ju, Li, Nautiyal, Mayank, Hellander, Andreas, Vats, Ekta, Singh, Prashant
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding
by: Nautiyal, Mayank, et al.
Published: (2026)

Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere
by: Ju, Li, et al.
Published: (2025)

ConDiSim: Conditional Diffusion Models for Simulation Based Inference
by: Nautiyal, Mayank, et al.
Published: (2025)

Variational Autoencoders for Efficient Simulation-Based Inference
by: Nautiyal, Mayank, et al.
Published: (2024)

OneFlowSBI: One Model, Many Queries for Simulation-Based Inference
by: Nautiyal, Mayank, et al.
Published: (2026)

PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models
by: Nautiyal, Mayank, et al.
Published: (2025)

Epistemic Uncertainty Quantification For Pre-trained Neural Network
by: Wang, Hanjing, et al.
Published: (2024)

Epistemic Wrapping for Uncertainty Quantification
by: Sultana, Maryam, et al.
Published: (2025)

Label-wise Aleatoric and Epistemic Uncertainty Quantification
by: Sale, Yusuf, et al.
Published: (2024)

Flow Matching with Uncertainty Quantification and Guidance
by: Han, Juyeop, et al.
Published: (2026)

Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
by: Collas, Antoine, et al.
Published: (2025)

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
by: Van Katwyk, Peter, et al.
Published: (2025)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
by: Song, Haoze, et al.
Published: (2026)

ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
by: Li, Mingda, et al.
Published: (2025)

Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models
by: Berger, Julia, et al.
Published: (2026)

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
by: Bakman, Yavuz, et al.
Published: (2025)

Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
by: Zepf, Kilian, et al.
Published: (2023)

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
by: Wang, Tianyang, et al.
Published: (2025)

Generative Pre-training for Speech with Flow Matching
by: Liu, Alexander H., et al.
Published: (2023)

(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models
by: Kirsch, Andreas
Published: (2024)

Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs
by: Valdettaro, Filippo, et al.
Published: (2024)

Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
by: Gaur, Mudit, et al.
Published: (2025)

Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching
by: Miao, Fangran, et al.
Published: (2026)

Riemannian Flow Matching Policy for Robot Motion Learning
by: Braun, Max, et al.
Published: (2024)

Fast and Robust Visuomotor Riemannian Flow Matching Policy
by: Ding, Haoran, et al.
Published: (2024)

Boltzmann Generators for Condensed Matter via Riemannian Flow Matching
by: Hoffmann, Emil, et al.
Published: (2026)

Soft-Label Training Preserves Epistemic Uncertainty
by: Singh, Agamdeep, et al.
Published: (2025)

Riemannian Variational Flow Matching for Material and Protein Design
by: Zaghen, Olga, et al.
Published: (2025)

Rethinking Aleatoric and Epistemic Uncertainty
by: Smith, Freddie Bickford, et al.
Published: (2024)

Total Variation Rates for Riemannian Flow Matching
by: Guan, Yunrui, et al.
Published: (2026)

Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch
by: Cai, Xu, et al.
Published: (2025)

Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
by: Marco, Adriel Sosa, et al.
Published: (2025)

Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning
by: Zhao, Zhizhong, et al.
Published: (2025)

FlowMM: Generating Materials with Riemannian Flow Matching
by: Miller, Benjamin Kurt, et al.
Published: (2024)

Uncertainty Quantification of Data Shapley via Statistical Inference
by: Wu, Mengmeng, et al.
Published: (2024)

Toward efficient resource utilization at edge nodes in federated learning
by: Alawadi, Sadi, et al.
Published: (2023)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
by: Staliūnaitė, Ieva Raminta, et al.
Published: (2026)

Multidimensional Uncertainty Quantification via Optimal Transport
by: Kotelevskii, Nikita, et al.
Published: (2025)

Risk-aware Classification via Uncertainty Quantification
by: Sensoy, Murat, et al.
Published: (2024)

DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
by: Wang, Yingxu, et al.
Published: (2026)