Deep Dynamic Probabilistic Canonical Correlation Analysis
Not on the map. Why?
Abstract (arXiv)
This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D2PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilistic extensions of Canonical Correlation Analysis (CCA), D2PCCA captures nonlinear latent dynamics and supports enhancements such as KL annealing for improved convergence and normalizing flows for a more flexible posterior approximation. D2PCCA naturally extends to multiple observed variables, making it a versatile tool for encoding prior knowledge about sequential datasets and providing a probabilistic understanding of the system's dynamics. Experimental validation on real financial datasets demonstrates the effectiveness of D2PCCA and its extensions in capturing latent dynamics.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 7 Feb 2025.
Builds on
11 of 24 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2016Variational Inference: A Review for Statisticianscited by 1,466
- 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
- 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
- 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
- 2019Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishingcited by 191
- 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
- 2020Dynamical Variational Autoencoders: A Comprehensive Reviewcited by 127
- 2020Improving Sequential Latent Variable Models with Autoregressive Flowscited by 8
- 2017Probabilistic partial least squares model: Identifiability, estimation and applicationcited by 3
Built on by
Citing papers per year: 2025: 1.
1 paper.