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..
Semi-Discrete Normalizing Flows through Differentiable Tessellation
Authors: Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show improvements over existing methods across a range of structured data modalities. |
| Researcher Affiliation | Industry | Meta AI |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Open source code is available at https://github.com/facebookresearch/semi-discrete-flow. |
| Open Datasets | Yes | We use unprocessed data sets from the UCI database [9]. |
| Dataset Splits | Yes | We then take 80% as train, 10% as validation, and 10% as the test set. |
| Hardware Specification | No | The paper states "All models were trained... on a single GPU," but does not specify the model or any other hardware details. |
| Software Dependencies | No | The paper mentions "Adam optimizer [22]" and refers to "torchdiffeq, 2018" from [5] for FFJORD, but does not list specific version numbers for multiple key software components like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | All models were trained using the Adam optimizer [22] with a learning rate of 1e-3, cosine annealing scheduler, and a batch size of 256 for 500 epochs on a single GPU. |