VINA: Variational Invertible Neural Architectures
#Variational Invertible Neural Architectures #Normalizing Flows #Invertible Neural Networks #Generative Modeling #Theoretical Guarantees #Machine Learning #Ocean-acoustic Inversion
📌 Key Takeaways
- Researchers introduced VINA, a unified framework for INNs and NFs with theoretical guarantees
- The framework addresses a critical gap in approximation quality under realistic assumptions
- The approach provides both theoretical performance guarantees and practical guidelines
- The effectiveness was demonstrated on a realistic ocean-acoustic inversion problem
📖 Full Retelling
Researchers Shubhanshu Shekhar and colleagues from an unspecified institution introduced VINA: Variational Invertible Neural Architectures in a paper published on arXiv on February 24, 2026, addressing a critical gap in the theoretical foundations of neural network architectures used for generative modeling and inverse problems. The paper presents a unified framework for Invertible Neural Networks (INNs) and Normalizing Flows (NFs) based on variational unsupervised loss functions, drawing inspiration from formulations in related areas such as generative adversarial networks and the Precision-Recall divergence. This novel approach provides theoretical performance guarantees that quantify posterior accuracy for INNs and distributional accuracy for NFs under assumptions that are weaker and more practically realistic than those used in prior work. The significance of this research lies in its dual contribution to both theory and practice, with extensive case studies that distill general design principles and practical guidelines for implementing these architectures. To demonstrate real-world applicability, the authors tested their approach on a realistic ocean-acoustic inversion problem, showing how theoretical advances can translate to practical solutions in complex domains.
🏷️ Themes
Machine Learning, Neural Networks, Theoretical Computer Science, Scientific Research
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Original Source
--> Computer Science > Machine Learning arXiv:2602.20480 [Submitted on 24 Feb 2026] Title: VINA: Variational Invertible Neural Architectures Authors: Shubhanshu Shekhar , Mohammad Javad Khojasteh , Ananya Acharya , Tony Tohme , Kamal Youcef-Toumi View a PDF of the paper titled VINA: Variational Invertible Neural Architectures, by Shubhanshu Shekhar and 4 other authors View PDF Abstract: The distinctive architectural features of normalizing flows , notably bijectivity and tractable Jacobians, make them well-suited for generative modeling. Invertible neural networks build on these principles to address supervised inverse problems, enabling direct modeling of both forward and inverse mappings. In this paper, we revisit these architectures from both theoretical and practical perspectives and address a key gap in the literature: the lack of theoretical guarantees on approximation quality under realistic assumptions, whether for posterior inference in INNs or for generative modeling with NFs. We introduce a unified framework for INNs and NFs based on variational unsupervised loss functions, inspired by analogous formulations in related areas such as generative adversarial networks and the Precision-Recall divergence for training normalizing flows. Within this framework, we derive theoretical performance guarantees, quantifying posterior accuracy for INNs and distributional accuracy for NFs, under assumptions that are weaker and more practically realistic than those used in prior work. Building on these theoretical results, we conduct extensive case studies to distill general design principles and practical guidelines. We conclude by demonstrating the effectiveness of our approach on a realistic ocean-acoustic inversion problem. Comments: 57 pages, 11 figures, 5 tables Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20480 [cs.LG] (or arXiv:2602.20480v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.20480 Focus to lear...
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