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..
Compositional Sculpting of Iterative Generative Processes
Authors: Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi Jaakkola
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We offer empirical results on image and molecular generation tasks. and 6 Experiments. |
| Researcher Affiliation | Collaboration | Timur Garipov1 Sebastiaan De Peuter2 Ge Yang1,4 Vikas Garg2,5 Samuel Kaski2,3 Tommi Jaakkola1 1MIT CSAIL 2Aalto University 3University of Manchester 4Institute for Artificial Intelligence and Fundamental Interactions 5Yai Yai Ltd |
| Pseudocode | Yes | Algorithm A.1 Compositional Sculpting: classifier training |
| Open Source Code | Yes | Project codebase: https://github.com/timgaripov/compositional-sculpting. |
| Open Datasets | Yes | three diffusion models trained to generate MNIST [56] digits {0, 1, 2, 3} in two colors: cyan and beige. and [56] Yann Le Cun. The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/, 1998. |
| Dataset Splits | No | The paper mentions training steps and batch sizes but does not specify explicit train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | All models were trained with a single Ge Force RTX 2080 Ti GPU. and All models were trained with a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions using PyTorch [68] and optimizers like Adam [59] and Ada Delta [67] but does not provide specific version numbers for these software components (e.g., 'PyTorch 1.x' or 'Python 3.x'). |
| Experiment Setup | Yes | We used Adam optimizer [59] with learning rate 0.001, and pre-train the base models for 20 000 steps with batch size 16 (16 trajectories per batch). and The score model was trained using Adam optimizer [59] with a learning rate decreasing exponentially from 10 2 to 10 4. We performed 200 training steps with batch size 32. |