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..
Learning to Infer Graphics Programs from Hand-Drawn Images
Authors: Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We trained our network by sampling specs S and target images I for randomly generated scenes1 and maximizing Pθ[S|I], the likelihood of S given I, with respect to model parameters θ, by gradient ascent. We trained on 105 scenes... Experiment 1: Figure 4. To evaluate which components of the model are necessary to parse complicated scenes, we compared the neural network with SMC against the neural network by itself... Experiment 2: Figures 5 7. We evaluated, but did not train, our system on 100 real hand-drawn figures... Experiment 3: Table 5; Figure 8; Supplement Section 4. We compare synthesis times for our learned search policy with 4 alternatives... |
| Researcher Affiliation | Academia | Kevin Ellis MIT EMAIL Daniel Ritchie Brown University EMAIL Armando Solar-Lezama MIT EMAIL Joshua B. Tenenbaum MIT EMAIL |
| Pseudocode | No | The paper describes algorithms and presents a grammar (Table 2) but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code and data are available at https://github.com/ellisk42/Tik Z. |
| Open Datasets | Yes | We trained our network by sampling specs S and target images I for randomly generated scenes1... We trained on 105 scenes... We evaluated, but did not train, our system on 100 real hand-drawn figures... The code and data are available at https://github.com/ellisk42/Tik Z. |
| Dataset Splits | No | The paper mentions training on '10^5 scenes' and evaluating on '100 real hand-drawn figures' but does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | We trained on 105 scenes, which takes a day on an Nvidia Titan X GPU. |
| Software Dependencies | No | The paper mentions using tools like 'Sketch tool [1]' but does not provide specific version numbers for any software, libraries, or programming languages (e.g., Python, PyTorch) used in the experiments. |
| Experiment Setup | No | The paper states 'Supplement Section 1 gives the full details of the architecture and training of this network' but does not provide specific hyperparameter values, optimizer settings, or other detailed training configurations in the main text. |