DisKO: Deep Koopman Learning in Distribution Space from Unpaired Snapshots
cs.LG, math.DS
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/zhenyiizhang/DeepRUOTv2
Sources
- A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
- Approximation capability of neural networks on spaces of probability measures and tree-structured domains
- Deep Sets
- Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks