:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Galperin, Daniel, Köthe, Ullrich
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.06940
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Analyzing Generative Models by Manifold Entropic Metrics
by: Galperin, Daniel, et al.
Published: (2024)

Learning Distances from Data with Normalizing Flows and Score Matching
by: Sorrenson, Peter, et al.
Published: (2024)

Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions
by: Hummerich, Sander, et al.
Published: (2025)

On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
by: Draxler, Felix, et al.
Published: (2024)

Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows
by: Sorrenson, Peter, et al.
Published: (2025)

Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
by: Saegert, Paul, et al.
Published: (2026)

Free-form Flows: Make Any Architecture a Normalizing Flow
by: Draxler, Felix, et al.
Published: (2023)

Learning Distributions on Manifolds with Free-Form Flows
by: Sorrenson, Peter, et al.
Published: (2023)

Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation
by: Rousselot, Armand, et al.
Published: (2026)

TRADE: Transfer of Distributions between External Conditions with Normalizing Flows
by: Wahl, Stefan, et al.
Published: (2024)

BayesFlow: Learning complex stochastic models with invertible neural networks
by: Radev, Stefan T., et al.
Published: (2020)

Lifting Architectural Constraints of Injective Flows
by: Sorrenson, Peter, et al.
Published: (2023)

OOD Detection with immature Models
by: Montazeran, Behrooz, et al.
Published: (2025)

The Principle of Uncertain Maximum Entropy
by: Bogert, Kenneth, et al.
Published: (2023)

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
by: Schmitt, Marvin, et al.
Published: (2024)

Diffusion Models in Simulation-Based Inference: A Tutorial Review
by: Arruda, Jonas, et al.
Published: (2025)

Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning
by: Müller, Jens, et al.
Published: (2023)

Learning and Improving Backgammon Strategy
by: Galperin, Gregory R.
Published: (2025)

Consistency Models for Scalable and Fast Simulation-Based Inference
by: Schmitt, Marvin, et al.
Published: (2023)

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
by: Schmitt, Marvin, et al.
Published: (2023)

Unsupervised Ordering for Maximum Clique
by: Min, Yimeng, et al.
Published: (2025)

Sensitivity-Aware Amortized Bayesian Inference
by: Elsemüller, Lasse, et al.
Published: (2023)

On-line Policy Improvement using Monte-Carlo Search
by: Tesauro, Gerald, et al.
Published: (2025)

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
by: Chen, Hao, et al.
Published: (2025)

Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models
by: Karczewski, Rafał, et al.
Published: (2025)

Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows
by: Rabenstein, Georg, et al.
Published: (2024)

Unsupervised Domain Transfer with Conditional Invertible Neural Networks
by: Dreher, Kris K., et al.
Published: (2023)

Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
by: Lee, Jonghyun, et al.
Published: (2024)

DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
by: Zisling, Hedi, et al.
Published: (2025)

Unsupervised Graph Clustering with Deep Structural Entropy
by: Zhang, Jingyun, et al.
Published: (2025)

EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy
by: Huang, Yihong, et al.
Published: (2024)

Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
by: Hu, Jiaheng, et al.
Published: (2024)

Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints
by: Wu, Yuxuan, et al.
Published: (2024)

Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision
by: Zhang, Zeyang, et al.
Published: (2024)

Disentangled Representation Learning via Flow Matching
by: Chi, Jinjin, et al.
Published: (2026)

Unsupervised Learning of Disentangled Representations from Video
by: Denton, Remi, et al.
Published: (2017)

Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection
by: Hou, Yue, et al.
Published: (2025)

What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning
by: Jia, Yaning, et al.
Published: (2025)

Curiosity & Entropy Driven Unsupervised RL in Multiple Environments
by: Dewan, Shaurya, et al.
Published: (2024)

From Concepts to Components: Concept-Agnostic Attention Module Discovery in Transformers
by: Su, Jingtong, et al.
Published: (2025)