Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variational Schrödinger Diffusion Models
Authors: Wei Deng, Weijian Luo, Yixin Tan, Marin Biloš, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we test the algorithm in simulated examples and observe that VSDM is efficient in generations of anisotropic shapes and yields straighter sample trajectories compared to the single-variate diffusion. We also verify the scalability of the algorithm in real-world data and achieve competitive unconditional generation performance in CIFAR10 and conditional generation in time series modeling. |
| Researcher Affiliation | Collaboration | 1Machine Learning Research, Morgan Stanley, NY 2Peking University 3Duke University 4Meta AI (FAIR). |
| Pseudocode | Yes | Algorithm 1 Variational Schr odinger Diffusion Models (VSDM). |
| Open Source Code | Yes | See code in https://github.com/pkulwj1994/diff_instruct |
| Open Datasets | Yes | We choose the CIFAR10 dataset as representative image data to demonstrate the scalability of the proposed VSDM on generative modeling of high-dimensional distributions. |
| Dataset Splits | Yes | Let {(t1, x1), . . . , (tn, xn)}, x Rd, denote a single multivariate time series. Given a dataset of such time series we want to predict the next P values xn+1, . . . , xn+P . In probabilistic modeling, we want to generate forecasts from learned p(xn+1:n+P |x1:n). |
| Hardware Specification | Yes | We train the VSDM model from scratch on two NVIDIA A100-80G GPUs for two days and generate images from the trained VSDM with the Euler Maruyama numerical solver with 200 discretized steps for generation. |
| Software Dependencies | No | The paper refers to existing code bases (FB-SDE, diffusion distillation code base) for implementation details but does not provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use a batch size of 256 and a learning rate of 1e-4. |