Cover Image

Symplectic Generative Networks (SGNs): A Hamiltonian Framework for Invertible Deep Generative Modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Aich, Agnideep, Aich, Ashit
Format:	Preprint
Published:	2025
Subjects:	Machine Learning 68T07, 37J39, 65P10, 62B10, 53D22, 94A17
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
by: Aich, Agnideep, et al.
Published: (2025)

Discretization of Dirac systems and port-Hamiltonian systems: the role of the constraint algorithm
by: Barbero-Liñán, María, et al.
Published: (2025)

A new sparsity promoting residual transform operator for Lasso regression
by: Xiao, Yao, et al.
Published: (2025)

A translation of "The Geometry of the Group of Symplectic Diffeomorphisms"
by: Polterovich, Leonid
Published: (2025)

Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
by: Gu, Hyemin, et al.
Published: (2022)

Analysis of the Chaotic Itinerancy Phenomenon using Entropy and Clustering
by: Mierski, Nikodem, et al.
Published: (2025)

The Predictive-Causal Gap: An Impossibility Theorem and Large-Scale Neural Evidence
by: Liu, Kejun
Published: (2026)

Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data
by: Diepeveen, Willem, et al.
Published: (2026)

Variable Projected Augmented Lagrangian Methods for Generalized Lasso Problems
by: Aleotti, Stefano, et al.
Published: (2025)

AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI
by: Shastry, Praveen, et al.
Published: (2025)

Retraction maps: A seed of geometric integrators. Part II: Symmetry and reduction
by: Liñán, María Barbero, et al.
Published: (2025)

Drinfeld Correspondence in Infinite Dimensions
by: Rahangdale, Praful
Published: (2026)

Volume-Preserving Transformers for Learning Time Series Data with Structure
by: Brantner, Benedikt, et al.
Published: (2023)

Pairs of differential forms: a framework for precontact geometry
by: Gràcia, Xavier, et al.
Published: (2026)

Energy-Based Modeling and Structure-Preserving Discretization of Physical Systems
by: Rashid, M. H. M
Published: (2025)

Universal Shuffle Asymptotics: Sharp Privacy Analysis in the Gaussian Regime
by: Shvets, Alex
Published: (2026)

Streaming Compression of Scientific Data via weak-SINDy
by: Russo, Benjamin P., et al.
Published: (2023)

Phase space integrity in neural network models of Hamiltonian dynamics: A Lagrangian descriptor approach
by: Hasmi, Abrari Noor, et al.
Published: (2026)

Learning Hamiltonian flows from numerical integrators and examples
by: Fang, Rui, et al.
Published: (2025)

Approximating Symplectic Realizations: A General Framework for the Construction of Poisson Integrators
by: Cabrera, Alejandro, et al.
Published: (2024)

The Bayesian SIAC filter
by: Glaubitz, Jan, et al.
Published: (2025)

Data-Driven Discovery of Feature Groups in Clinical Time Series
by: Sergeev, Fedor, et al.
Published: (2025)

Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines
by: Fokoué, Ernest
Published: (2026)

Multi-Hypothesis Prediction for Portfolio Optimization: A Structured Ensemble Learning Approach to Risk Diversification
by: Dominguez, Alejandro Rodriguez, et al.
Published: (2025)

Approximating $f$-Divergences with Rank Statistics
by: Stein, Viktor, et al.
Published: (2026)

Cross Validation for Correlated Data in Regression and Classification Models, with Applications to Deep Learning
by: Yuval, Oren, et al.
Published: (2025)

The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
by: Cribeiro-Ramallo, Jose, et al.
Published: (2025)

Mixed Semi-Supervised Generalized-Linear-Regression with Applications to Deep-Learning and Interpolators
by: Yuval, Oren, et al.
Published: (2023)

A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation
by: Kayal, Tanmay, et al.
Published: (2025)

A Unified Framework for Lifted Training and Inversion Approaches
by: Wang, Xiaoyu, et al.
Published: (2025)

Bayesian--AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence
by: Chatterjee, Debashis
Published: (2025)

How Regularization Terms Make Invertible Neural Networks Bayesian Point Estimators
by: Heilenkötter, Nick
Published: (2025)

Entropic Conditional Central Limit Theorem and Hadamard Compression
by: Ma, Zhi-Ming, et al.
Published: (2024)

Hamiltonian Floer theory on surfaces
by: Connery-Grigg, Dustin
Published: (2021)

A supervised learning scheme for computing Hamilton-Jacobi equation via density coupling
by: Cui, Jianbo, et al.
Published: (2024)

Complementary Characterization of Agent-Based Models via Computational Mechanics and Diffusion Models
by: Garrone, Roberto
Published: (2025)

Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
by: Chen, Yongsheng, et al.
Published: (2025)

Event-Level Probabilistic Prediction of Extreme Rainfall over India Using Physics-Gated Latent Dynamics
by: Neelan, Arun Govind
Published: (2026)

Approximation of Splines in Wasserstein Spaces
by: Justiniano, Jorge, et al.
Published: (2023)

Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series
by: Sherkhon, Azimov, et al.
Published: (2025)