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..
Multi-objects Generation with Amortized Structural Regularization
Authors: Taufik Xu, Chongxuan LI, Jun Zhu, Bo Zhang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that ASR outperforms the DGM baselines in terms of inference performance and sample quality. |
| Researcher Affiliation | Academia | Kun Xu, Chongxuan Li, Jun Zhu , Bo Zhang Dept. of Comp. Sci. & Tech., Institute for AI, THBI Lab, BNRist Center, State Key Lab for Intell. Tech. & Sys., Tsinghua University, Beijing, China EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Our code is attached in the supplementary materials for reproducing. |
| Open Datasets | Yes | In this section, we present the empirical results of ASR on two dataset: Multi-MNIST [8] and Multi-Sprites [12] |
| Dataset Splits | No | The paper specifies training and test data sizes (e.g., '40000 training samples' and '2000 images are used as the test data'), but it does not explicitly state a validation dataset split. |
| Hardware Specification | No | The paper mentions implementing the model using TensorFlow but provides no specific details about the hardware used for running experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | We implement our model using Tenwor Flow [1] library. (The paper mentions 'Tenwor Flow' but does not specify a version number for this or any other software dependency.) |
| Experiment Setup | Yes | We use the Adam optimizer [18] with learning rate as 0.001, β1 = 0.9, and β2 = 0.999. We train models with 300 epochs with batch size as 64. |